System and method for predicting acute cardiopulmonary events and survivability of a patient

ABSTRACT

A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. §120 as acontinuation of U.S. application Ser. No. 14/199,495 filed on Mar. 6,2014 and titled METHOD OF PREDICTING ACUTE CARDIOPULMONARY EVENTS ANDSURVIVABILITY OF A PATIENT, which is herein incorporated by reference inits entirety. U.S. application Ser. No. 14/199,495 claims the benefitunder 35 U.S.C. §121 as a division of U.S. application Ser. No.13/868,605 filed on Apr. 23, 2013 and titled, METHOD OF PREDICTING ACUTECARDIOPULMONARY EVENTS AND SURVIVABILITY OF A PATIENT, now U.S. Pat. No.8,668,644, which is incorporated herein by reference in its entirety.U.S. application Ser. No. 13/868,605 claims the benefit under 35 U.S.C.§120 as a continuation of U.S. application Ser. No. 13/047,348 filed onMar. 14, 2011 and titled, METHOD OF PREDICTING ACUTE CARDIOPULMONARYEVENTS AND SURVIVABILITY OF A PATIENT, which is incorporated herein byreference in its entirety. U.S. application Ser. No. 13/047,348 claimspriority under 35 U.S.C. §119(e) to U.S. Provisional Application Ser.No. 61/313,822 filed on Mar. 15, 2010.

FIELD OF THE INVENTION

The invention relates to a method of predicting acute cardiopulmonary(ACP) events and survivability of a patient. The invention also relatesto a system for predicting acute cardiopulmonary events andsurvivability of a patient.

BACKGROUND OF THE INVENTION

Triage is an important part of any Emergency Medical Response. This isthe clinical process of rapidly screening large numbers of patients toassess severity and assign appropriate priority of treatment. Triage isa reality as medical resources are never enough for all patients to beattended instantaneously. It is thus important to be able to quicklyidentify patients of higher severity, who would need such resources moreurgently. Therefore, a device for automatic patient outcome (cardiacarrest and mortality) analysis could be helpful to conduct triage,especially in disaster or mass casualty situations, where demandoverwhelms resources.

Current triage systems are based on clinical judgment, traditional vitalsigns and other physiological parameters. They tend to be subjective,and are not so convenient and efficient for clinicians. Moreover, theclinical ‘vital signs’ including heart rate, respiratory rate, bloodpressure, temperature and pulse oximetry have not been shown tocorrelate well with short or long-term clinical outcomes.

SUMMARY OF THE INVENTION

According to embodiments of the invention, there is provided a method ofproducing an artificial neural network capable of predicting ACP eventsand the survivability of a patient, the method including: storing in anelectronic database patient health data, the patient health datacomprising a plurality of sets of data, each set having at least one ofa first parameter relating to heart rate variability data and a secondparameter relating to vital sign data, each set further having a thirdparameter relating to patient survivability; providing a network ofnodes interconnected to form an artificial neural network, the nodescomprising a plurality of artificial neurons, each artificial neuronhaving at least one input with an associated weight; and training theartificial neural network using the patient health data such that theassociated weight of the at least one input of each artificial neuron ofthe plurality of artificial neurons is adjusted in response torespective first, second and third parameters of different sets of datafrom the patient health data, such that the artificial neural network istrained to produce a prediction on the ACP events and survivability of apatient.

According to embodiments of the invention, there is provided a method ofpredicting the ACP events and survivability of a patient, the methodincluding: measuring a first set of parameters relating to heart ratevariability data of a patient; measuring a second set of parametersrelating to vital sign data of the patient; providing an artificialneural network comprising a network of interconnected nodes, the nodescomprising a plurality of artificial neurons, each artificial neuronhaving at least one input with an associated weight adjusted by trainingthe artificial neural network using an electronic database having aplurality of sets of data, each set having at least a parameter relatingto heart rate variability data and a parameter relating to vital signdata, each set further having a parameter relating to patientsurvivability; processing the first set of parameters and the second setof parameters to produce processed data suitable for input into theartificial neural network; providing the processed data as input intothe artificial neural network; and obtaining an output from theartificial neural network, the output providing a prediction on the ACPevents and survivability of the patient.

According to embodiments of the invention, there is provided a patientACP events and survivability prediction system including: a first inputto receive a first set of parameters relating to heart rate variabilitydata of a patient; a second input to receive a second set of parametersrelating to vital sign data of the patient; a memory module storinginstructions to implement an artificial neural network comprising anetwork of interconnected nodes, the nodes comprising a plurality ofartificial neurons, each artificial neuron having at least one inputwith an associated weight adjusted by training the artificial neuralnetwork using an electronic database having a plurality of sets of data,each set having at least a parameter relating to heart rate variabilitydata and a parameter relating to vital sign data, each set furtherhaving a parameter relating to patient survivability; a processor toexecute the instructions stored in the memory module to perform thefunctions of the artificial neural network and output a prediction onthe ACP events and survivability of the patient based on the first setof parameters and the second set of parameters; and a display fordisplaying the prediction on the ACP events and survivability of thepatient.

According to embodiments of the invention, there is provided a method ofpredicting the ACP events and survivability of a patient, the methodincluding: measuring a first set of parameters relating to heart ratevariability data of a patient; measuring a second set of parametersrelating to vital sign data of the patient; obtaining a third set ofparameters relating to patient characteristics; providing the first setof parameters, the second set of parameters and the third set ofparameters as sets of normalized data values, where required, to ascoring model implemented in an electronic database, the scoring modelhaving a respective category associated to each parameter of the firstset of parameters, the second set of parameters and the third set ofparameters, each category having a plurality of pre-defined valueranges, each of the plurality of value ranges having a pre-definedscore; determining a score for each parameter of the first set ofparameters, the second set of parameters and the third set of parametersby assigning the sets of normalized data to respective pre-defined valueranges, encompassing the sets of normalized data values, of theplurality of value ranges of the category associated to the respectiveparameter of the first set of parameters, the second set of parametersand the third set of parameters; obtaining a total score, being asummation of the score for each parameter of the first set ofparameters, the second set of parameters and the third set ofparameters, the total score providing an indication on the ACP eventsand survivability of the patient.

According to aspects of embodiments, a system for the detection ofimpending acute cardiopulmonary medical events that, left untreated,would with a reasonable likelihood result in either severe injury ordeath includes: an electro-cardiogram (ECG) module including a pluralityof electrodes for sensing a patient's ECG and having an ECG output; asensor for sensing a patient's physiologic parameter other than ECG; afirst input for receiving the ECG output; a second input for receivingsignals from the sensor for sensing a patient's physiologic parameterother than ECG; a third input constructed and arranged to receive:parametric information describing at least one element of a patient'sdemographic information; and parametric information describing apatient's medical history; a digitizing unit for digitizing the ECG andthe physiologic signal other than ECG; a housing containing a memoryunit and processing unit, for storing and processing, respectively, theECG, the physiologic signal other than ECG, patient demographicinformation and medical history; and a user communication unit; whereinthe processing unit calculates at least one measure of heart ratevariability (HRV), combines that at least one measure of HRV with atleast one parameter each of patient demographic information and medicalhistory, and calculates a statistical probability of an ACP event within72 hours of the calculation. The system may further be constructed andarranged to be carried by the patient in a wearable configuration. Thesensor may measure the perfusion status of the microvasculature. Thesensor may be a pulse oximeter. The system may further include: anelectromagnetic stimulator of physiologic tissue, which may stimulatecardiac tissue. The user communication unit may have key entry. Thethird input may be a key entry. The user communication unit may be inthe main housing. The user communication unit may be separate from themain housing. The user communication unit may be a display. Thestimulation may be pacing or the stimulation may be defibrillation. Thestimulation may be magnetic stimulation.

According to aspects of embodiments, a system for predicting mortalityof a patient being treated for trauma or as part of a mass casualtyoccurrence, includes: an electro-cardiogram (ECG) module including aplurality of electrodes for sensing a patient's ECG and having an ECGoutput; a sensor for sensing a patient's physiologic parameter otherthan ECG; a first input for receiving the ECG output; a second input forreceiving signals from the sensor for sensing a patient's physiologicparameter other than ECG; a third input constructed and arranged toreceive: parametric information describing at least one element of apatient's demographic information; and parametric information describinga patient's medical history; a digitizing unit for digitizing the ECGand the physiologic signal other than ECG; a housing containing a memoryunit and processing unit, for storing and processing, respectively, theECG, the physiologic signal other than ECG, patient demographicinformation and medical history; and a user communication unit; whereinthe processing unit calculates at least one measure of heart ratevariability (HRV), combines that at least one measure of HRV with atleast one parameter each of patient demographic information and medicalhistory, and calculates a statistical probability of mortality for thepatient. The system may be constructed and arranged to be carried by thepatient in a wearable configuration. The sensor may measure theperfusion status of the microvasculature. The sensor may be a pulseoximeter.

According to aspects of embodiments of the invention, a method oftreating a cardiac condition of a patient, includes: measuring heartrate variability (HRV) of the patient; measuring vital sign data of thepatient; predicting, using a computing apparatus constructed andarranged for the purpose, a likelihood of survival of the patient to oneor more selected time limits based on HRV in combination with themeasured vital sign data; and treating the cardiac condition asindicated by the vital sign data when the likelihood of survival of thepatient to one or more selected time limits is below a desiredthreshold. The method may further include: collecting at least one ofpatient demographic information and patient history information; whereinpredicting further comprises: computing the likelihood of survivaladditionally in view of the collected patient demographic informationand patient history information. The method may yet further include:selecting a time limit of between 4 and 24 hours or a time limit ofbetween 4 and 72 hours.

According to aspects of embodiments of the invention, an apparatus forpredicting a likelihood of survival of a patient to one or more selectedtime limits due to cardiac causes, includes: a heart rate sensor havinga heart rate output; a vital sign sensor having a vital sign output; acomputational module receiving the heart rate output and the vital signoutput, and performing: computing heart rate variability (HRV) from theheart rate output received; and computing the likelihood of survival ofthe patient to the one or more selected time limits due to cardiaccauses, from a combination of the HRV computed and the vital signoutput; and, an output device displaying to a user the likelihood ofsurvival of the patient to the one or more selected time limits due tocardiac causes. The apparatus may further include: a data input deviceconstructed and arranged to collect at least one of patient demographicinformation and patient history information; and computing thelikelihood of survival additionally in view of the collected patientdemographic information and patient history information. The apparatusmay yet further include: a time limit of between 4 and 24 hours or atime limit of between 4 and 72 hours.

The invention will be further illustrated in the following description,with reference to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to the sameparts throughout the different views. The drawings are not necessarilyto scale, emphasis instead generally being placed upon illustrating theprinciples of the invention. In the following description, variousembodiments of the invention are described with reference to thefollowing drawings, in which:

FIG. 1 is a flow chart illustrating a method, according to oneembodiment of the present invention, used to produce an artificialneural network capable of predicting the ACP events and survivability ofa patient.

FIG. 2 is a schematic representation of an artificial neural networkaccording to one embodiment of the present invention.

FIG. 3 is a schematic representation of an artificial neural networkaccording to one embodiment of the present invention.

FIG. 4 shows a block diagram of a system used to predict the ACP eventsand survivability of a patient.

FIG. 5 shows a flow chart, in accordance with embodiments of theinvention, implemented by a signal acquisition block.

FIG. 6 shows a flow chart, in accordance with embodiments of theinvention, implemented by a signal processing module.

FIG. 7 shows a flow chart, in accordance with embodiments of theinvention, implemented by a beat detection and post processing module.

FIG. 8 shows a flow chart, in accordance with embodiments of theinvention, implemented by a HRV parameter calculation module.

FIG. 9 shows a block diagram representation of how data flows in ananalysis block.

FIG. 10 shows a flow chart illustrating use of a system, in accordancewith embodiments of the invention, utilizing wireless technology.

FIG. 11 summarizes raw ECG data characteristics of patients.

FIG. 12 shows a flow chart, in accordance with embodiments of theinvention, illustrating how an ECG signal is pre-processed to calculateHRV parameters.

FIG. 13 shows how data extraction is performed.

FIG. 14 shows a flow chart illustrating a method, according to oneembodiment of the present invention, of predicting the ACP events andsurvivability of a patient.

FIG. 15 shows a schematic of a patient ACP events and survivabilityprediction system in accordance with embodiments of the invention.

FIG. 16 shows a schematic of a patient ACP events and survivabilityprediction system in accordance with embodiments of the invention.

FIG. 17 shows pictures of a patient ACP events and survivabilityprediction system in accordance with embodiments of the invention.

FIGS. 18 to 20 show snap shots of the output of a patient ACP events andsurvivability prediction system in accordance with embodiments of theinvention.

FIG. 21 shows a flow chart illustrating a method, according to oneembodiment of the present invention, used to predict the ACP events andsurvivability of a patient.

FIG. 22 shows a flow chart used by a validation system.

FIGS. 23, 24 and 25 respectively show classification results using vitalsigns, HRV measures, and combined features.

FIG. 26 shows results from using a different number of selected segmentsusing combined features.

FIG. 27 shows four different predictive strategies.

FIG. 28 shows results from different predictive strategies usingcombined features.

FIG. 29 shows classification results from using vital signs, HRVmeasures, and combined features.

FIGS. 30, 31 and 32 depict the performances of extreme learning machine(ELM) in terms of different number of hidden nodes.

FIG. 33 shows results from different predictive strategies usingcombined features.

FIG. 34 shows an embodiment of the invention in a wearable medicaldevice.

DETAILED DESCRIPTION

According to aspects of embodiments, a system is able to reliablypredict acute cardiopulmonary medical events that, left untreated, wouldwith a high likelihood result in either severe injury or death. Examplesof such acute cardiopulmonary (ACP) events would include cardiac orrespiratory arrest, hypovolemic shock particularly due to blunt traumainjury or acute decompensated heart failure.

Previous systems seeking to determine and predict patient morbidity andpatient mortality under various trauma, stress, and shock conditionshave included in the battery of signs monitored, heart rate variability(HRV). HRV measurement quantifies the variability over time of the R-Rinterval in the electrocardiographic signal of the patient. The R-waveof a particular heartbeat corresponds to the point in the cardiac cycleof the early systolic phase, and from a signal processing point of view,provides a reliable time-fiducial for making cardiac cycle intervalmeasurements. HRV is affected by the autonomic nervous system, whichconsists of the sympathetic nervous system (SNS) and the parasympatheticnervous system (PNS). Observed HRV is believed to be an indicator of thedynamic interaction and balance between the SNS and PNS, providing ameasure of nervous system competence. HRV serves as an indicator for thediagnosis and assessment of a variety of conditions that are affected bythe autonomic system ranging from congestive heart failure to sleepapnoea. For example, decreased HRV has been found to be a predictor ofincreased mortality in the elderly for coronary heart disease. DecreasedHRV is also seen after sudden cardiac arrest and in patients withdiseases such as diabetes, uraemia and hypertension. Unfortunately,heart rate variability alone, while being able to predict increasedmortality, is only a poor predictor of ACP events with any timespecificity.

A variability measure related to HRV is T-wave alternans which is ameasure of the variation in the recovery of the myocardium during thediastolic (relaxation) phase, and measures the fluctuations in theamplitude of the T-wave of the ECG. Because of the need to measureminute fluctuations in ECG amplitude, it is relatively susceptible topatient motion-induced artifacts and so not useful for continuousmonitoring of a patient's ECG.

In accordance with aspects of embodiments, for example in triagesystems, it would be of value to be able to reliably predict acutecardiopulmonary medical events that, left untreated, would with a highlikelihood result in either severe injury or death. Examples of suchacute cardiopulmonary (ACP) events would include cardiac or respiratoryarrest, hypovolemic shock particularly due to blunt trauma injury oracute decompensated heart failure. Conventional clinical signs, symptomsand physiologic measurements provide little warning for these types ofevents. For instance, implantable cardioverter defibrillators (ICDs) orwearable external defibrillators such as the Lifevest (ZOLL Medical)will continuously analyze the patient's electrocardiographic (ECG)signal during their daily activities and deliver a life-savingelectrical shock to the heart.

In U.S. Application 2009/0234410A1, a system is described for theprediction of heart failure decompensation. This, and similar, systemsrequire the detection of a cardiac arrhythmia via the ECG, whichunfortunately limits the duration of predictive forecast accuracy. Forinstance, arrhythmia detectors on ICDs and wearable defibrillators onlydetect a shockable event after the patient is in a lethal arrhythmiarequiring a shock. Despite extensive research, utilizing arrhythmiaanalysis for the reliable prediction of impending ACP events has beenproblematic, lacking in both predictive accuracy as well as event timespecificity (prediction of when the event might occur). U.S.2009/0234410 may utilize heart rate variability in conjunction with thearrhythmia analysis, but again, the use of the arrhythmia detector willlimit the predictive accuracy.

More sophisticated analytic methods of cardiac arrhythmias such asT-wave alternans also require very accurate measurement of ECG voltagesto better than 1 microvolt typically and thus tend to be verysusceptible to signal artifact generated in systems where the ECG ismonitored on a relatively continuous basis such as a wearable monitoringand therapeutic device. U.S. Pat. No. 4,957,115 describes a system usingECG arrhythmia analysis along with other physiological measurements togenerate a probability score of impending death due to a cardiovascularevent. Other systems, such as that described in U.S. Pat. No. 7,272,435,might be used in a stress test laboratory where patients are viewedunder controlled conditions unlike those conditions that would likely beencountered on a wearable device. Under such strictly controlledconditions, noise-susceptible measurement techniques such as T-wavealternans might be applicable.

U.S. Pat. Nos. 6,665,559 and 5,501,229 describe systems that determine aprobability of cardiovascular risk based on serial comparisons of ECGarrhythmia analysis. It would thus be advantageous, according to aspectsof embodiments of the invention, to have a system that is both morerobust in the presence of ECG signal artifacts often encountered duringcontinuous monitoring from an external wearable device, and furtheradvantageous to have a system that is able to predict with somereliability when an ACP event is most likely to occur.

Aspects of embodiments of the invention combine HRV with other vitalsign data, as distinct from US Published Patent Application 2007/112,275A1, which describes a system which alerts a user on any vital sign goingout of a desired range. Further, aspects of embodiments of the inventionpredict the likelihood of occurrence of acute cardiopulmonary (ACP)events by combining HRV with other vital sign data, as compared with USPublished Patent Application 2007/276,275 A1, which describes predictingmorbidity and mortality due to an entirely different and unrelated typeof injury, traumatic brain injury, using HRV combined with one or moreother vital signs.

Measurements of HRV data according to aspects of embodiments provide ameasure of the interaction between the autonomic nervous system and thecardiovascular system. While HRV has become a well-known technique usedby researchers in attempts to predict ACP events (See for instance,Insights from the Study of Heart Rate Variability, P. K. Stein, R. E.Kleiger, Annu. Rev. Med. 1999. 50:249-61), as Stein et al. point out,HRV alone is insufficient to predict, with any reasonable degree ofaccuracy, future clinical events.

Aspects of embodiments of the invention differ from commercial devicesfor HRV analysis currently available in the market in yet other ways.Some commercial HRV analysis devices are bulky. Aspects of embodimentsare more portable and therefore field ready, so as to be convenient forroutine use in hospitals and for outfield environments such asambulances. Moreover, aspects of embodiments do more than simplycorrelate some HRV measures with particular abnormalities ofcardiovascular system, as commercial devices currently do. Aspects ofembodiments, in a portable package, predict risk scores for patientoutcomes. Some commercial devices are portable but have limitedfunctions. Experienced clinicians interpret the outputs and some currentcommercial devices only provide simple information such as the healthcondition of a normal person. Aspects of some embodiments thus alsoimprove upon existing commercial devices, which lack the combination ofportability and ability of automatically predicting patient outcomesthat is crucial to triage.

In one embodiment of the invention, there is provided a patient-wearabledevice such as device 10, shown in overall view in FIG. 34. Thepatient-worn device may include a waist-encompassing belt 14 of suitablefabric, webbing or the like, and may incorporate sprung elements thebelt having a low-profile connector or buckle 16 and a shoulder strap 18of like material connected between front and rear portions of the belt.First and second sensing and pulse electrode assemblies 20 are carriedrespectively on belt 14 and shoulder strap 18. Belt 14 also carries anelectronics housing 24 which may have a supporting strap connection 26with strap 18 and electrical conductors, diagrammatically indicated at28 and 30, for receiving electrical signals from and deliveringelectrical pulses to the respective electrode assemblies 20. Assemblies20 have respective sensing electrodes 22 and pulse electrodes 32.

In use of the device as thus far described, assemblies 20 are held incomfortable contact with a patient's chest wall and continuously monitorand detect the heart rhythm by means of the respective sensingelectrodes 22. Alternatively, sensing electrodes may be traditionaldisposable ECG electrodes placed on the patient's skin in a locationseparate from the pulse electrodes 32. Device 10 may be worn over acomfortable undergarment 34, such as a T-shirt, which may have apertures36 that receive the respective electrode assemblies 20. Attachments 38,such as patches of loop and pile Velcro-type fabric, may be providedbetween belt 14, strap 18 and the undergarment.

The housing for the electrode assemblies 20 may contain signalconditioning and amplification electronics for the EGG electrode. TheEGG electrode 22 may be capacitive, conductive carbon, or any otherdesign that permits long-term use without skin irritation. It isunderstood that the printed circuits of the respective electrodes areconnected to the pulse generator 24 through conductors 28 and 30.

A sensor for measuring a second physiologic parameter such as a pulseoximeter 38 is used to measure additional physiologic status of thepatient. In the case of the pulse oximeter the physiologic parameter isthat of tissue perfusion.

The sensor might also be impedance plethysmography (IP), known to thoseskilled in the art. IP is accomplished by measuring small variations inthe electrical impedance of the tissue underlying the sense electrodes,typically by applying a small current to the electrodes and measuringthe induced voltage. As the volume of the tissue changes, as a result ofphysiological activity such as blood perfusion or as increased air inthe lungs with respiration, its electrical impedance also changes. Thusthe physiologic parameter sensed can be both blood flow and respirationsimultaneously via the same set of impedance electrodes. It is alsopossible, and known to those skilled in the art that the ECG electrodes22 can also be used for both impedance measurements as well as ECGsimultaneously as the impressed current for IP is typically at 30 kHz orhigher and thus can be filtered from the input signal to the ECGamplifiers prior to processing, since ECG signals contain relevantfrequencies no higher than 100 Hz. More than one sensor may be providedto obtain multiple measures for two or more physiological parameters.

The ECG signal may be detected using passive devices such as anelectrode making an electrical contact, using sticky pads, pastes or gelwith the at least one patient's skin surface. Other means such as anactive device, which need not necessarily contact the at least onepatient's skin surface to detect the patient's ECG signal, may be used.Such an active device may be an insulated bioelectrode (IBE). The IBEmay measure the electric potential on the skin without resistiveelectrical contact and with very low capacitive coupling. The IBE may beconnected, wirelessly or via cable, to a processing unit. To achieve awireless IBE, a wireless node platform may be integrated into the IBE.An example of a system that may function with a wireless IBE is the“Tmote Sky” platform, using three wireless IBEs to form a 3-lead system.The “Tmote Sky” platform has an 802.15.4 radio interface at 250 Kbps andis controlled by the MSP430F1611 microcontroller.

Referring to FIG. 4, the system 400 has three main functional blocks: asignal acquisition block 402, a signal processing block 404 and ananalysis block 406. The signal acquisition block 402 has sensor andsignal conditioning hardware 408 for acquiring an ECG signal and othervital signs from a patient 401. The sensor and signal conditioninghardware 408 may include sensors that detect ECG signals, and otherphysiological parameters such as blood pressure, tissue perfusion suchas SpO2 and respiration rate.

The signal acquisition block 402 has a data acquisition (DAQ)electronics 410, which in one embodiment contains the signalconditioning circuits used for processing output from the sensor andsignal conditioning hardware 408. The signal conditioning circuits aredesigned to process signals from these sensors. The signal conditioningcircuits comprise electronic components that perform functions such asisolation and amplification of the various signals measured by thesensors as well as conversion of the analog signals to digital signals.The DAQ electronics 410 communicate the digitized ECG and otherphysiological parameters to the processing unit 430. The processing unitcontains circuit elements known to those skilled in the art: aprocessing unit such as a microprocessor; a program storage circuit suchas a disk drive or solid state storage element such as a ROM or Flashmemory; a dynamic data storage element such as DRAM; a communicationcircuit such as a serial data channel, Bluetooth, USB, etc. forcommunicating with both the DAQ 410 and external devices such as a WiFinetwork or cellular network; a user interface circuit containing adisplay, audio channel and speaker, a touchscreen interface andswitches; a battery and power supply circuit. An input panel alsoaccepts additional information such as age and gender of the patient401.

The signal processing block 404 includes a signal processing module 426,a vital sign module 420 and a patient information module 418. Thecircuitry may be configured in such a way as to optimize functions, withthe Signal Processing Module 426 and Analysis Module 406 functions beingprovided by a digital signal processor (DSP) chip such as the TexasInstruments Blackfin processor family, and the user interface and otherfunctions being provided by a general purpose microprocessor such asDual-Core Intel Xeon Processor running a Linux operating system. By theword “module”, we refer only to the particular functions performed bythe processing unit 430; the module boundary in the figure may or maynot correspond to actual circuitry. The signal processing module 426includes an ECG pre-processing module 412, a beat detection and postprocessing module 414, and a HRV parameter calculation module 416. TheECG pre-processing module 412 processes raw ECG data from the signalacquisition block 402 to suppress unwanted signals such as noise, motionartifacts and power line interference which may affect the accuracy ofHRV parameters eventually extracted from the ECG data. The beatdetection and post processing module 414 acts on de-noised signal fromthe ECG pre-processing module 412 to detect a heartbeat and to excludenon-sinus beats during post-processing. The duration between consecutivesinus beats are compiled into an RRI (beat to beat interval) sequencefrom which HRV parameters are computed. Extraction is preferably from anECG signal derived from the patient's sinus rhythm.

In one embodiment of the present invention, extracting the heart ratevariability data comprises filtering the ECG signal to remove noise andartifacts; locating a QRS complex within the filtered ECG signal;finding a RR interval between successive R waves of the QRS complex; andprocessing the sequence of information within the RR interval to obtainthe heart rate variability data.

In one embodiment of the present invention, a band pass filter is usedto filter the ECG signal and locate the QRS complex. A band pass filterwith an operating frequency range wider than the frequency components ofthe QRS complex has to be used. The frequency components of the QRScomplex lie between 10 to 25 Hz. Thus, in one embodiment of the presentinvention, the operation frequency range of the band pass filter isbetween about 5 Hz to about 28 Hz.

In one embodiment of the present invention, the R wave may be located asfollows. A maximum peak data value first occurring in the filtered ECGsignal is located. An upper amplitude threshold and a lower amplitudethreshold from the located maximum peak value are determined. A peakvalue and minimum values on either side of the peak value are located.In this embodiment of the invention, either side refers to the left andright sides of the peak value. The conditions of whether the peak valueis above the upper amplitude threshold, while the minimum values arebelow the lower amplitude threshold are met is checked. If theconditions are met, the location of the peak value is denoted as an Rposition. The location of the minimum value occurring closest on theleft side of the R position is denoted as a Q position, and the locationof the minimum value occurring closest on the right hand side of the Rposition is denoted as an S position. With reference to a time scalethat the filtered ECG signal is plotted against, the Q position occursat where the minimum value first occurs before the R position, while theS position occurs at where the minimum value first occurs after the Rposition. The location of a QRS peak within the filtered ECG signal isthus determined.

In one embodiment of the present invention, where a 1D array of ECGsample points x(n) are provided, the upper and lower amplitudethresholds (T_(upper) and T_(lower)) are set after finding the maximumvalue (ref_peak) within the first few seconds of data. The thresholdsare defined as:T _(upper)=ref_peak+0.4*ref_peakT _(lower)=ref_peak−0.35*ref_peakThen an R wave is said to occur at the point i if the followingconditions are met,

-   -   x(i) lies between T_(upper) and T_(lower);    -   x(i+1)−x(i)<0; and    -   x(i)−x(i−1)>0;        where the R-peak is the point with maximum value.

The positions of other R waves within the filtered ECG signal may belocated by iterating the process of: locating another peak value andlocating other minimum values on either side of the another peak value.When the another peak value is above the upper amplitude threshold whilethe other minimum values are both below the lower threshold, thelocation of the peak value is denoted as an R position. The location ofthe minimum value occurring closest on the left side of the R positionis denoted as a Q position and the location of the minimum valueoccurring closest on the right side of the R position is denoted as an Sposition. In this manner, the location of another QRS peak isdetermined.

Processing the sequence of information within the RR interval mayfurther comprise removing outliers from the sequence of informationwithin the RR interval. A median value and a standard deviation valuefor the RR interval may be found. A tolerance factor based on thestandard deviation value may be calculated. A portion of informationthat lies within the RR interval spanning either side of the medianvalue by the tolerance factor may be retained. Heart rate variabilitydata may be obtained from the retained portion of information and theremaining portion of the information from the sequence of informationmay be discarded.

In embodiments of the invention, the heart rate variability data mayinclude time domain data, frequency domain data and geometric domaindata.

The time domain data may include information on any one or more of thefollowing parameters: mean of RR intervals (mean RR), standard deviationof RR intervals (STD), mean of the instantaneous heart rate (mean HR),standard deviation of the instantaneous heart rate (STD_HR), root meansquare of differences between adjacent RR intervals (RMSSD), number ofconsecutive RR intervals differing by more than 50 ms (NN50), andpercentage of consecutive RR intervals differing by more than 50 ms(pNN50).

The frequency domain data may include information on any one or more ofthe following parameters: power in very low frequency range (<=0.04 Hz)(VLF), power in low frequency range (0.04 to 0.15 Hz) (LF), power inhigh frequency range (0.15 to 0.4 Hz) (HF), total power which isestimated from the variance of NN intervals in the segment and ismeasured in ms² (TP), ratio of LF power to HF power (LF/HF), LF power innormalized units: LF/TP−VLF)×100 (LFnorm), and HF power in normalizedunits: HF/TP−VLF)×100 (HFnorm).

The geometric domain data may include information on any one of thefollowing data: total number of all RR intervals divided by height ofhistogram of intervals (HRV Index) and base width of triangle fit intoRR histogram using least squares method (TINN).

In embodiments of the invention, the vital sign data may include any oneor more of the following: systolic blood pressure, diastolic bloodpressure, pulse rate, pulse oximetry, respiratory rate, glasgow comascale (GCS), pain score, temperature. The vital sign measurement may beeither a continuous variable in the form of a waveform. The vital signmeasurement may also be a measurement taken at a single point in time,or the vital sign measurement may be a series of measurements, typicallysampled at regular intervals that may sometimes be stored in the form ofso-called trend data.

In embodiments of the invention, the patient health data used to trainthe artificial neural network may be standard deviation of theinstantaneous heart rate (STD_HR), power in low frequency range (0.04 to0.15 Hz) in normalized units (LFnorm), age, pulse rate, pulse oximetry,systolic blood pressure and diastolic blood pressure.

In embodiments of the invention, the measured first set of parametersare standard deviation of the instantaneous heart rate (STD_HR) andpower in low frequency range (0.04 to 0.15 Hz) in normalized units(LFnorm); and the measured second set of parameters are age, pulse rate,pulse oximetry, systolic blood pressure and diastolic blood pressure.

The patient health data includes parameters relating to heart ratevariability data, vital sign data, patient survivability and patientcharacteristics. The patient health data may include a plurality of setsof data, where each set of data may be formed from a single category ofthese parameters, i.e. either the first parameter relating to heart ratevariability, the second parameter relating to vital sign data, the thirdparameter relating to patient characteristics or a fourth parameterrelating to patient survivability. On the other hand, each set of datamay have a combination of categories of these parameters, such as atleast one of the first parameter relating to heart rate variability, thesecond parameter relating to vital sign data and the third parameterrelating to patient characteristics such as age, gender, or otherdemographic characteristic, as well as specific conditions in thepatient's health history such as diabetes, myocardial infarction, highblood pressure. Severity of the specific condition is also recorded andprovided to the system, such as the date of occurrence of the myocardialinfarction, the post-infarction ejection fraction or the percentageextent of the ventricular tissue damage. Other descriptors may be thespecific medications that a patient uses to treat various medicalconditions. A fourth parameter may be provided relating to patientsurvivability such as an outcome like survival to hospital discharge.The fourth parameter is used as a means of training the algorithm duringthe training phase of algorithm development and during use as a means ofimproving the accuracy by recording the predictive algorithm's actualaccuracy and making suitable modifications to improve that accuracy. Theset of data may not even necessarily include the parameter relating topatient survivability. Alternatively, each set of patient health datamay include all four parameters. It will thus be appreciated that withinthe patient health data, one set of data may not contain the same numberof parameters compared to another set of data. Further, the patienthealth data is stored as digital data converted from the form in whicheach of the four parameters is originally obtained (such as an analogsignal), whereby the original form of the obtained measurements.

Data for patient characteristics such as demographics, health historyand survivability may be communicated to the device 10 or system 400 viaa wireless network distributed through a hospital, such as 802.11.

According to embodiments of the present invention, a method of producingan artificial neural network capable of predicting the survivability ofa patient is provided. The method includes storing patient health datain an electronic database. The patient health data includes a pluralityof sets of data, each set having at least one of a first parameterrelating to heart rate variability data and a second parameter relatingto vital sign data. Each of the plurality of sets of data further has athird parameter relating to patient survivability. A network of nodesinterconnected to form an artificial neural network is provided. Thenodes include a plurality of artificial neurons, each artificial neuronhaving at least one input with an associated weight. The artificialneural network is trained using the patient health data such that theassociated weight of the at least one input of each artificial neuron ofthe plurality of artificial neurons is adjusted in response torespective first, second and third parameters of different sets of datafrom the patient health data. This results in the artificial neuralnetwork being trained to produce a prediction on the survivability of apatient.

The electronic database used to store patient health data may be amemory module such as a hard disk drive, an optical disc, or solid statedevices (for example thumb drives). During the training phase of thealgorithm, the patient health data may be obtained from hospital recordsor from conducting field studies of a pool of patient(s), where the poolincludes a group of patients acting as a control group. Thus, thepatient health data may include data of patients suffering from variousailments, patients who are healthy (i.e. having no symptoms ofillnesses), patients of various race and age and/or patients who areterminally ill.

It was earlier mentioned that vital sign data may be one of theparameters (referred to as the second parameter in the plurality of setsof data related to patient health) used to train the artificial neuralnetwork that can be used to implement a clinical decision supportprogram or device.

Vital sign data is defined as clinical measurements that indicate thestate of a patient's essential body functions. These measurements relateto systolic blood pressure, diastolic blood pressure, pulse rate, pulseoximetry, respiratory rate, glasgow coma scale (GCS), pain score andtemperature.

Training phase vital sign data may be obtained from hospital records orfrom conducting field studies of a pool of patient(s). When conductingfield studies, each vital sign may be measured as follows. For example,systolic blood pressure and diastolic blood pressure may be measuredusing a blood pressure measurement device such as the “statMAP™ Model7200” from “CardioCommand”. Alternatively, devices such as asphygmomanometer or a mercury manometer may be used. Pulse rate, pulseoximetry and respiratory rate may be measured using a pneumogram.Glasgow coma scale (GCS) refers to the degree of spontaneity of thepatient's physical (such as limbs, eyes) motor and/or verbal response toinstructions from a medical professional. Pain score refers to thedegree of response (such as adduction, pronation or extension of a limbor body part; flexion or withdrawal) to pain applied to the patient.Temperature may be recorded using a thermometer.

Turning to another parameter that may be used to train the artificialneural network, patient survivability (referred to as the thirdparameter in the plurality of sets of data related to patient health)refers to the outcome, i.e. either death or survival, of a patient.Thus, data on the patient survivability is typically associated with arespective set of both heart rate variability data and vital sign datafor the same patient.

Another parameter that may be used to train the artificial neuralnetwork is patient characteristics. Patient characteristics includeinformation such as patient age, gender and medical history. At theconclusion of the training phase, the parameters found to be mostrelevant to achieving a high level of accuracy will then be used asinputs to the real time detection system.

An electronic device may incorporate a processor or memory modulestoring instructions to implement the trained artificial neural network,so that the device can analyse health data of a patient being examined.The output of the electronic device can then be used to assist anoperator or a medical professional to predict the outcome of the patientand thereby make appropriate clinical decisions on how to treat thepatient.

In embodiments of the invention, the artificial neural network (ANN) maybe a mathematical model or computational model simulating the structureand/or functional aspects of a biological neural network. In embodimentsof the invention, the nodes of the ANN include at least one input (beingthe at least one actual input of the ANN), at least one artificialneuron and at least one output (being the at least one actual output ofthe ANN). The at least one artificial neuron may be present in a singlehidden layer of the ANN. In other embodiments of the invention where theANN has a plurality of artificial neurons, the plurality of artificialneurons may be distributed across one or more hidden layers. Where thereis more than one layer, each layer may be interconnected with a previousand a subsequent layer.

The artificial neurons may processes information using a connectionistapproach to computation. The ANN may be an adaptive system, where itchanges based on external or internal information that flows through theANN during the training or learning phase. Specifically, the weight (orstrength) of the connections (such as between adjacent artificialneurons, or between an input and an artificial neuron) within the ANN isadapted to change.

In embodiments of the invention, the first parameter (heart ratevariability data), the second parameter (vital sign data) or acombination of the first parameter and the second parameter may beclassified as feature vectors of the patient health data. The artificialneural network may be trained with the feature vectors.

The artificial neural network may be implemented as instructions storedin a memory that when executed by a processor cause the processor toperform the functions of the artificial neural network.

In embodiments of the invention, the artificial neural network may bebased on support vector machine architecture, wherein the associatedweight of the at least one input of each artificial neuron of theplurality of artificial neurons is initialized from a library used bythe support vector machine. The support vector machine may have anaggregated output comprising a decision function, the decision functiongiven by

${f(x)} = {{sgn}\left( {{\sum\limits_{i = 1}^{N}\;{\alpha_{i}y_{i}{k\left( {x,x_{i}} \right)}}} + b} \right)}$wherein sgn( ) is a sign function, (x,x_(i)) is set of feature vector,k(x,x_(i)) is a kernel matrix constructed by x and x_(i), y_(i) is 1 or−1, which is the label of feature vector x_(i), α_(i) and b areparameters used to define an optimal decision hyperplane so that themargin between two classes of patterns can be maximized in the featurespace.

In embodiments of the invention, the artificial neural network may bebased on an extreme learning machine architecture, wherein theassociated weight of the at least one input of each artificial neuron ofthe plurality of artificial neurons is initialized through randomselection by the extreme learning machine. The artificial neural networkmay be realized as a single-layer feed-forward network, whereby theprediction on the survivability of the patient is derived from thefunction,

${{f_{\overset{\sim}{N}}\left( x_{j} \right)} = {{\sum\limits_{i = 1}^{\overset{\sim}{N}}\;{\beta_{i}{g\left( {{w_{i} \cdot x_{j}} + b_{i}} \right)}}} = {{t_{j}\mspace{14mu} j} = 1}}},\ldots\mspace{14mu},N$wherein x_(j) is an input vector to an input of one of the plurality ofartificial neurons for j=1, 2, . . . , N input vectors; w_(i) is theassociated weight of the input of the artificial neuron receiving thex_(j) input vector; g(w_(i)·x_(j)+b_(i)) is an output of the artificialneuron receiving the x_(j) input vector . . . for i=1, 2, . . . , Nartificial neurons; β_(i) is the output weight vector that associates ani^(th) hidden neuron with a respective output neuron; and b_(i) is thebias for the i^(th) hidden neuron.

In embodiments of the invention, training of the artificial neuralnetwork may be based on back-propagation learning.

In embodiments of the invention, the back-propagation learning may usethe Levenberg-Marquardt algorithm.

In embodiments of the invention, each of the plurality of artificialneurons of the artificial neural network may have an activationfunction, the activation function being selected from a group offunctions comprising hardlim, sigmoid, sine, radial basis and linear.

In embodiments of the invention, the sequence of information within theRR interval may be partitioned into non-overlapping segments; and thenon-overlapping segments may be used to train the artificial neuralnetwork. A length of signal within the RR interval of each of thefiltered ECG signal may be extracted. The length of signal may bepartitioned into non-overlapping segments; and at least one of thenon-overlapping segments may be selected to train the artificial neuralnetwork.

In embodiments of the invention, each of the non-overlapping segmentsmay be of substantially equal length. In embodiments of the invention,the non-overlapping segments may have a fixed length.

According to embodiments of the present invention, a method ofpredicting the survivability of a patient is provided. The methodincludes measuring a first set of parameters relating to heart ratevariability data of a patient. A second set of parameters relating tovital sign data of the patient is also measured. An artificial neuralnetwork including a network of interconnected nodes is provided, thenodes including a plurality of artificial neurons. Each artificialneuron has at least one input with an associated weight adjusted bytraining the artificial neural network using an electronic databasehaving a plurality of sets of data. Each set of data has at least aparameter relating to heart rate variability data and a parameterrelating to vital sign data, each set of data further having a parameterrelating to patient survivability. The method includes processing thefirst set of parameters and the second set of parameters to produceprocessed data suitable for input into the artificial neural network.The processed data is provided as input into the artificial neuralnetwork. An output is then obtained from the artificial neural network,the output providing a prediction on the survivability of the patient.

In embodiments of the invention, the processed data of the first set ofparameters and the processed data of the second set of parameters may berepresented as a feature vector.

In embodiments of the invention, the processed data may be the first setof parameters and the second set of parameters being represented asnormalized data.

In embodiments of the invention, the processed data may be partitionedinto non-overlapping segments, so that the input into the artificialneural network may include sets of one or more of the non-overlappingsegments of processed data. A result may be obtained for each of thesets of one or more of the non-overlapping segments of processed data,so that each of the results may be considered to predict thesurvivability of the patient.

In embodiments of the invention, majority voting may be used todetermine the prediction on the survivability of the patient, themajority voting represented by the function

$\hat{y} = {\overset{2}{\max\limits_{j = 1}}\mspace{11mu}{\sum\limits_{m = 1}^{M}\; D_{m,j}}}$wherein D_(m,j) is an intermediate variable for final decision making,D_(m,j) assigned a value of 1 if a m^(th) classifier chooses class j inthe decision ensemble, and 0 otherwise.

In embodiments of the invention, the result of the artificial neuralnetwork may be coded as a two class label. The method of predicting thesurvivability of a patient may then further include applying alabel-based algorithm to each of the two class label results to decidethe output from the artificial neural network, thereby providing aprediction on the survivability of the patient.

In embodiments of the invention, the prediction on the survivability ofthe patient is either death or survival of the patient.

In embodiments of the invention, a patient survivability predictionsystem includes: a first input to receive a first set of parametersrelating to heart rate variability data of a patient; a second input toreceive a second set of parameters relating to vital sign data of thepatient; and a memory module storing instructions to implement anartificial neural network. The artificial neural network includes anetwork of interconnected nodes, the nodes including a plurality ofartificial neurons. Each artificial neuron has at least one input withan associated weight adjusted by training the artificial neural networkusing an electronic database having a plurality of sets of data. Eachset of data has at least one a parameter relating to heart ratevariability data and a parameter relating to vital sign data. Each setof data further has a parameter relating to patient survivability. Thepatient survivability prediction system further includes a processor toexecute the instructions stored in the memory module to perform thefunctions of the artificial neural network and output a prediction onthe survivability of the patient based on the first set of parametersand the second set of parameters; and a display for displaying theprediction on the survivability of the patient.

In embodiments of the invention, the patient survivability predictionsystem may further include a port to receive the first set of parametersfrom the first input and the second set of parameters from the secondinput.

In embodiments of the invention, the patient survivability predictionsystem may further include a first port to receive the first set ofparameters from the first input; and a second port to receive the secondset of parameters from the second input.

According to embodiments of the invention, a method of predicting thesurvivability of a patient is provided. The method includes: measuring afirst set of parameters relating to heart rate variability data of apatient; measuring a second set of parameters relating to vital signdata of the patient and obtaining a third set of parameters relating topatient characteristics. The first set of parameters, the second set ofparameters and the third set of parameters are provided as sets ofnormalized data values, where required, to a scoring model implementedin an electronic database. The scoring model has a respective categoryassociated to each parameter of the first set of parameters, the secondset of parameters and the third set of parameters. Each category has aplurality of pre-defined value ranges, each of the plurality of valueranges having a pre-defined score. A score for each parameter of thefirst set of parameters, the second set of parameters and the third setof parameters is determined by assigning the sets of normalized data torespective pre-defined value ranges, encompassing the sets of normalizeddata values, of the plurality of value ranges of the category associatedto the respective parameter of the first set of parameters, the secondset of parameters and the third set of parameters. A total score, beinga summation of the score for each parameter of the first set ofparameters, the second set of parameters and the third set of parametersis obtained. The total score provides an indication on the survivabilityof the patient.

It will be appreciated that in embodiments of the invention, onlyselected parameters of the first set of parameters, the second set ofparameters and the third set of parameters may be provided to thescoring model implemented in the electronic database. For instance, thethird set of parameters may entirely not be obtained from the patient orprovided to the scoring model. In embodiments of the invention, furtherparameters of patient health data may be measured and provided to thescoring model.

The scoring model may be any suitable process or algorithm,implementable in an electronic database, which can assign a score toeach range of values within each category associated to each parameterof the first set of parameters, the second set of parameters and thethird set of parameters. For instance, the scoring model may be based ona mathematical model using logistic regression, such as univariateanalysis.

In embodiments of the invention, the score may be a numerical value,which may be determined according to statistical information or standardmedical information. The numerical value of the pre-defined score mayalso depend on the pre-defined value range, which the pre-defined scoreis assigned to, in the respective category. In embodiments of theinvention, adjacent pre-defined value ranges within the same categorymay each have an assigned pre-defined score of the same numerical value.It will also be appreciated that pre-defined value ranges withindifferent categories may each have an assigned pre-defined score of thesame numerical value.

The scope of the pre-defined value ranges may depend on the category towhich they belong to and may be determined according to statisticalinformation or standard medical information. The scope of a pre-definedvalue range for a category associated to a parameter of the first set ofparameters may be different to the scope of a pre-defined value rangefor a category associated to a parameter of the second set ofparameters. In embodiments of the invention, there may be no overlapbetween pre-defined value ranges of the same category.

In embodiments of the invention, assigning sets of normalized data torespective pre-defined value ranges may involve first determining whichcategory of the scoring model the normalized data belongs to.Subsequently, it may be determined which one of the pre-defined valueranges the normalized data value belongs to, by ascertaining that thenumerical value of the normalized data value falls within or isencompassed by the scope of the respective pre-defined value range.

In embodiments of the invention, the scoring model may further include aplurality of risk categories, each category having a pre-defined rangeof values. The method of predicting the survivability of a patient mayfurther include assigning the total score to the category having thepre-defined range of values encompassing the total score, to determinewhich of the plurality of risk categories the total score belongs to.

While embodiments of the invention will be shown and described withreference to specific embodiments, it should be understood by thoseskilled in the art that various changes in form and detail may be madetherein without departing from the spirit and scope of the invention asdefined by the appended claims. The scope of the invention is thusindicated by the appended claims and all changes which come within themeaning and range of equivalency of the claims are therefore intended tobe embraced.

It will be appreciated that common numerals, used in the relevantdrawings, refer to components that serve a similar or the same purpose.

FIG. 1 is a flow chart 100 illustrating a method, according to oneembodiment of the present invention, used to produce an artificialneural network capable of predicting the survivability of a patient.

The method includes three steps 102, 104 and 106.

In step 102, patient health data is stored in an electronic database.The patient health data includes a plurality of sets of data, each sethaving at least one of a first parameter relating to heart ratevariability data and a second parameter relating to vital sign data.Each of the plurality of sets of data further has a third parameterrelating to patient survivability.

In step 104, a network of nodes interconnected to form an artificialneural network (ANN) is provided. The nodes include a plurality ofartificial neurons, each artificial neuron having at least one inputwith an associated weight. The artificial neural network (ANN) providedin step 104 may be a mathematical model or computational modelsimulating the structure and/or functional aspects of a biologicalneural network.

In step 106, the artificial neural network is trained using the patienthealth data such that the associated weight of the at least one input ofeach artificial neuron of the plurality of artificial neurons isadjusted in response to respective first, second and third parameters ofdifferent sets of data from the patient health data. This results in theartificial neural network being trained to produce a prediction on thesurvivability of a patient.

As mentioned above, artificial neural networks (such as the ANN providedin step 104) are based on the way the human brain approaches patternrecognition tasks, providing an artificial intelligence based approachto solve classification problems. A model is ‘learned’ during a trainingprocess using previously known input-output pairs. The trained model isthen tested with new data.

Various artificial neural network topologies are available, includingsingle-layer and multi-layer feedforward networks. Such ANNs aretypically BP (backpropagation) based, whereby weights of hidden layersare adjusted during training to minimize an error function.

In embodiments of the invention, the nodes of the ANN include at leastone input (being the at least one actual input of the ANN), at least oneartificial neuron and at least one output (being the at least one actualoutput of the ANN).

FIG. 2 is a schematic representation of an artificial neural network 200according to one embodiment of the present invention. With reference tothe flow chart 100 shown in FIG. 1, the artificial neural network 200may be provided in the step 104.

In the embodiment shown in FIG. 2, the ANN 200 is a single hidden-layerfeedforward network (SLFN). The ANN 200 has an input layer 202, a hiddenlayer 204 and an output layer 206.

The input layer 202 includes one or more input nodes 202 ₁, 202 ₂, 202₃, . . . and 202 _(n). While FIG. 2 shows that the hidden layer 204 hasonly three artificial neurons 204 ₁, 204 ₂ and 204 ₃, it will beappreciated that any number of artificial neurons may be used. Theoutput layer has two output nodes 206 ₁ and 206 ₂.

The output of each of the input nodes 202 ₁, 202 ₂, 202 ₃, . . . and 202_(n) may be connected to an input of every one of the artificial neurons204 ₁, 204 ₂ and 204 ₃ in the hidden layer 204. However, for the sake ofsimplicity, only a few such connections between the input layer 202 andthe hidden layer 204 is illustrated in FIG. 2. Similarly, the output ofeach of the artificial neurons 204 ₁, 204 ₂ and 204 ₃ may be connectedto an input of every one of the output nodes 206 ₁ and 206 ₂ in theoutput layer 206. In this manner, a network of interconnected nodes isformed.

Each of the artificial neurons 204 ₁, 204 ₂ and 204 ₃ has at least oneinput. For simplicity, only inputs for one of the artificial neurons arelabeled in FIG. 2, being inputs 208 ₁ and 208 ₂ for the artificialneuron 204 ₁. Each input of the respective artificial neurons (204 ₁,204 ₂ and 204 ₃) has an associated weight.

In training the ANN 200 to predict the survivability of a patient, theassociated weight of the at least one input of each artificial neuron(for example inputs 208 ₁ and 208 ₂ of the artificial neuron 204 ₁) isadjusted in response to respective first, second and third parameters ofdifferent sets of data from the patient health data. With reference tostep 102 of flow chart 100 of FIG. 1, the first parameter relates toheart rate variability data, the second parameter relates to vital signdata and the third parameter relates to patient survivability.

The trained ANN 200 can then be used to assist clinical decisions onwhether a patient exhibiting certain symptoms will survive or will die,i.e. the trained ANN 200 can assist in the prediction on thesurvivability of the patient.

The trained ANN 200 may be used to predict the survivability of thepatient as follows. A first set of parameters relating to heart ratevariability data of the patient is measured. A second set of parametersrelating to vital sign data of the patient is also measured. The firstset of parameters and the second set of parameters are processed toproduce processed data suitable for input into the trained artificialneural network 200. The processed data is provided as input 212 into theartificial neural network 200, for example at the input layer 202. Anoutput 214 is then obtained from the artificial neural network 202, theoutput 214 providing a prediction on the survivability of the patient.

FIG. 3 is a schematic representation of an artificial neural network 300according to one embodiment of the present invention. With reference tothe flow chart 100 shown in FIG. 1, the artificial neural network 300may be provided in the step 104.

In the embodiment shown in FIG. 3, the ANN 300 is a multi-layerfeedforward network. The ANN 300 has an input layer 302, a hidden layer304 and an output layer 306.

The main difference between the ANN 300 of FIG. 3 and the ANN 200 ofFIG. 2 is that the ANN 300 of FIG. 3 has several layers ofinterconnected artificial neurons 304 _(n) instead of having a singlelayer of artificial neurons. Each layer of artificial neurons 304 _(n)may be interconnected with a previous and a subsequent layer ofartificial neurons 304 _(n).

Another difference is that it takes a longer time to train the ANN 300(compared to training the ANN 200 of FIG. 2) to predict thesurvivability of a patient, as there are more artificial neurons 304_(n) having inputs (for instance 308 ₁ and 308 ₂) where their associatedweights have to be adjusted in response to patient health data.

Functionally, the hidden layer 304 still works in the same manner as thehidden layer 204 of the ANN 200. Similarly, the input layer 302 and theoutput layer 306 function in the same manner as the input layer 202 andthe output layer 206 respectively of the ANN 200. Thus, the functions ofthe input layer 302, the hidden layer 304 and the output layer 306 arenot further elaborated.

In a further embodiment of the invention, the system may be used as ameans of triaging patients such as in combat situations, other masstrauma situations such as multi-vehicular automobile accidents orterrorist incidents. The trained ANN 300 can be used to assist clinicaldecisions on whether a patient exhibiting certain symptoms will surviveor will die, i.e. the trained ANN 300 can assist in the prediction onthe survivability of the patient.

FIG. 4 shows a block diagram of a system 400 used to predict thesurvivability of a patient, the system 400 built in accordance to anembodiment of the invention.

The system 400 acquires ECG signals real-time, filter noise and ectopicbeats, generate HRV parameters and combine these with other vitalparameters such as blood pressure, oxygen saturation, respiratory rate,pulse rate and age into a composite triage score. The aim of the system400 is to have a portable, field usable, integrated device that willassist medical staff in rapid, real-time triage of patients based onrisk prediction. Such a system 400 would be particularly applicable inmass disaster scenarios as well as high volume patient load situationslike the Emergency Department.

There are known systems that use HRV as a predictor, but such systemsfocused mainly on specific patient conditions such as sepsis and headtrauma. Further, available HRV analysis software packages either requirethe RR interval (ECG beat-to-beat intervals) to be generated externallyor have limited functionality in terms of the available features. Thesepackages work ‘off-line’ using the entire recording or on a selectedsegment and do not have automatic methods to identify and isolatenon-sinus beats before computing HRV parameters.

The system 400 has the following advantages over known existing systems:

-   1. Dynamically acquire and process raw ECG signals from a patient to    reduce the effects of noise and other artifacts such as movement and    interference.-   2 Generate the RR interval sequence after automatically isolating    non-sinus beats and artifacts.-   3. Compute and display time and frequency domain HRV parameters.-   4. Acquire and display real time vital signs including blood    pressure, respiration rate and SpO2 (Saturation of peripheral    oxygen) using appropriate sensors and signal conditioning circuits.-   5. Compute and display a risk score(s) related to the various    possible patient outcomes.-   The system 400 is able to perform the above functions in    “real-time”.

The system 400 has three main functional blocks: a signal acquisitionblock 402, a signal processing block 404 and an analysis block 406.

The signal acquisition block 402 has sensor and signal conditioninghardware 408 for acquiring an ECG signal and other vital signs from apatient 401. The sensor and signal conditioning hardware 408 may includesensors that detect ECG signals, blood pressure, SpO2 and respirationrate.

The signal acquisition block 402 has a data acquisition (DAQ) card 410,which in one embodiment contains the signal conditioning circuits usedfor processing output from the sensor and signal conditioning hardware408. The signal conditioning circuits are designed to process signalsfrom these sensors. The signal conditioning circuits comprise electroniccomponents that perform functions such as isolation and amplification ofthe various signals measured by the sensors. The output of each signalconditioning circuit is a signal with a peak amplitude of about 1V.

The DAQ card 410 may also act as an interface to a computer. An inputpanel also accepts additional information such as age and gender of thepatient 401. The DAQ card is used to perform analog-to-digitalconversion of the acquired signals from the sensor and signalconditioning hardware 408 for interfacing with a computer for furtherprocessing. A National Instruments PCMCIA or USB card may be used forthis purpose. The DAQ card should preferably have a sampling rate ofaround 10 kHz and use 16-bit quantization.

The signal processing block 404 includes a signal processing module 426,a vital sign module 420 and a patient information module 418.

The signal processing module 426 includes an ECG pre-processing module412, a beat detection and post processing module 414, and a HRVparameter calculation module 416.

The ECG pre-processing module 412 processes raw ECG data from the signalacquisition block 402 to suppress unwanted signals such as noise, motionartifacts and power line interference which may affect the accuracy ofHRV parameters eventually extracted from the ECG data. The beatdetection and post processing module 414 acts on denoised signal fromthe ECG pre-processing module 412 to detect a heartbeat and to excludenon-sinus beats during postprocessing. The duration between consecutivesinus beats are compiled into an RRI (beat to beat interval) sequencefrom which HRV parameters are computed.

The HRV parameter calculation module 416 is used to extract HRVparameters from the output of the beat detection and post processingmodule 414.

The patient information module 418 receives input regarding additionalinformation about the patient 401, such as age, gender, Glasgow ComaScore (GCS) and medical history. The normalization is carried out withanalysis block 406.

Vital sign data such as blood pressure, SpO2 and respiration rate isprocessed by the vital sign module 420. The normalization is carried outwith analysis block 406.

The analysis block 406 includes a HRV parameter and patient informationanalysis module 422 and a risk score module 424. It will be appreciatedthat the ANN in accordance with embodiments of the invention (see forinstance FIGS. 1 to 3) is implemented in the analysis block 406.

The analysis block 406 computes HRV parameters obtained from the signalprocessing block 404 and compiles them into feature sets using resultsobtained from patient health data obtained from hospital records or fromconducting field studies. Patient 401 demographics such as age, gender,Glasgow Coma Score, etc, which can be keyed into the system, are alsoused in the analysis along with the vital signs of the patient 401. Arisk score providing a prediction on different outcomes such as death,ward admission and intensive care unit (ICU) admission of the patient401 is computed and may be displayed on a computer screen.

The signal processing block 404 and the analysis block 406 may beimplemented using software, such as “LabView” deployed on a hand heldelectronic device 430 (illustrated in FIG. 4 as a dotted block). The“LabView” program performs signal acquisition, noise removal, beatdetection, post-processing, computation of HRV parameters and display ofthe risk scores as described above. In this manner, the hand heldelectronic device 430 acts as a standalone device, where a suitabledeployment platform for the hand held electronic device 430 would be“CompactRIO” by “National Instruments”.

In further detail, for an ECG signal from the signal acquisition block402, noise removal is performed within the “LabView” program using a1-50 Hz band-pass filter which suppresses high frequency interference aswell as low frequency variations due to baseline wander and shift, andmotion artifacts. The denoised signal is displayed on a screen 432.

In another embodiment (not shown), the signal acquisition block 402, thesignal processing block 404 and the analysis module 406 are integratedinto a single hand held electronic device.

Beat detection is performed from a 1D array of ECG sample points x(n),as follows. In one embodiment of the present invention, where a 1D arrayof ECG sample points x(n) are provided, the upper and lower amplitudethresholds (T_(upper) and T_(lower)) are set after finding the maximumvalue (ref_peak) within the first few seconds of data. The thresholdsare defined as:T _(upper)=ref_peak+0.4*ref_peakT _(lower)=ref_peak−0.35*ref_peakThen a QRS peak is said to occur at the point i if the followingconditions are met,

-   -   x(i) lies between T_(upper) and T_(lower);    -   x(i+1)−x(i)<0; and    -   x(i)−x(i−1)>0;        where the R-peak is the point with maximum value.

The positions of other QRS peaks within the filtered ECG signal may belocated by iterating the process of: locating another peak value andlocating other minimum values on either side of the another peak value.When the another peak value is above the upper amplitude threshold whilethe other minimum values are both below the lower threshold, thelocation of the peak value is denoted as an R position. The location ofthe minimum value occurring closest on the left side of the R positionis denoted as a Q position and the location of the minimum valueoccurring closest on the right side of the R position is denoted as an Sposition. In this manner, the location of another QRS peak isdetermined.

The above technique of beat detection automatically generates RRinterval sequences from given ECG data, after correcting for ectopicbeats and noise, with minimal user input. The beat detection techniquewas tested using data from known databases (for example the MIT-BIHarrhythmia database, website:http://www.physionet.org/physiobank/database/mitdb/) and results werefound to match closely to manually annotated values. The technique wasalso tested on ambulance ECG data, which is subject to higher levels ofnoise and motion artifacts, with good results.

From detected QRS complexes, the processed RR interval (RRI) sequencecan be obtained. The processed RRI is used to calculate the followingHRV parameters, from which time domain and frequency domain measures maybe measured:

Examples of time domain measures are:

Time Domain Measures

-   1. Average length of the RR interval (aRR): Mean of all sinus RR    intervals (N-N) in the sequence-   2. Standard deviation of all N-N interval (SDNN)-   3. Mean heart rate (mean HR)-   4. Standard deviation of all instantaneous heart rate values (SDHR)-   5. Square root of the mean squared differences of successive N-N    intervals (RMSSD): The square root of the mean of the sum of the    squares of differences between adjacent N-N intervals-   6. HRV triangular index: Total number of all N-N intervals divided    by the height of the histogram of all NN intervals.-   7. Baseline width of a triangle fit into the N-N interval histogram    using a least squares technique (TINN)    Examples of frequency domain measures are:    Frequency Domain Measures

Frequency domain measures are calculated based on the power spectrum ofthe RRI sequence which is generated using a Lomb-Scargle periodogram.The following parameters are then calculated:

-   1. Total power (TP): Variance of N-N intervals over the segment till    0.4 Hz-   2. VLF: Power in very low frequency range <0.04 Hz-   3. LF: Power in low frequency range. 0.04-0.15 Hz-   4. HF: Power in high frequency range. 0.15-0.4 Hz-   5. LF norm: LF power in normalized units: LF norm=LF/TP−VLF)×100%-   6. HF norm: HF power in normalized units: HF norm=HF/TP−VLF)×100%-   7. LF/HF: Ratio of LF/HF

In addition to the above HRV parameters, a user can also input otherpatient 401 parameters such as age, gender, Glasgow Coma Score,respiration rate, blood pressure, SpO2 and heart rate. These parametersfor the patient 401 are used to calculate a risk score to predict thesurvivability of the patient 401. In calculating the risk score, it willbe appreciated that the artificial neural network within the analysisblock 406 has been trained as outlined in FIGS. 1 to 3 above. The outputof the analysis block 406 will be a risk score which will classify thepatient as being ‘high’, ‘medium’ or ‘low’ risk for each of the hospitaloutcomes including death, hospital admission and ICU admission.

Each of the FIGS. 5 to 9 show a flow chart, in accordance withembodiments of the invention, implemented by a respective functionalblock of the system 400 of FIG. 4.

FIG. 5 shows a flow chart 500, in accordance with embodiments of theinvention, implemented by the signal acquisition block 402 of FIG. 4.

In step 502 a patient is chosen to perform prediction on survivability.

In step 504, the patient's ECG signal, pulse rate, pulse oximetry, bloodpressure and clinical information are obtained. Examples of clinicalinformation include age, gender and medical history (eg cancer,diabetes, heart disease).

In step 506, the patient's ECG signal, pulse rate, pulse oximetry, bloodpressure and clinical information is sent to a data acquisition (DAQ)card. All the information from step 506 will be acquired by the DAQ cardsent as data to a computer or stand-alone device in real-time.

In step 508, the information from step 506 is sampled and converted froman analog signal into digital data in step 510.

In step 512, the signal acquisition block 402 (see FIG. 4) checks therecording length of digital ECG data that has been collected. Forreliable calculation of HRV parameters from the digital data obtained instep 510, it has been noticed that a recording length of at least sixminutes is required. If six minutes worth of digital ECG data has yet tobe collected, the flow chart 500 returns to step 504. On the other hand,if six minutes of digital ECG data has been recorded, the flow chartstops at step 514. In step 514, the digital ECG data is stored, alongwith vital signs and clinical information of the patient, into thecomputer or stand-alone device.

FIG. 6 shows a flow chart 600, in accordance with embodiments of theinvention, implemented by the signal processing module 426 of FIG. 4.

The flow chart 600 begins with step 602 with the ECG pre-processingmodule 412 having a raw ECG data and vital sign data as input.

Raw ECG data may not always contain a single continuous length of datapoints. Often, leads may be removed or settings may have been changed,resulting in gaps in the data. Hence in step 604, the calibration valuesare removed or trimmed, the data segments separated and concatenated toget one continuous stream of data.

In step 606, the signal processing module 426 has unfiltered ECG datawith calibration values trimmed. The effects of noise and artifacts inunfiltered ECG data are well known. The low amplitude of the ECG signalmakes it highly susceptible to noise and interference from a variety ofsources. These include high-frequency noise, power line interference,baseline wander, motion artifact, and other low frequency distortions.The presence of noise can result in false positives at the QRS detectionstage and thus injects errors into the generation of the HRV sequenceand in the subsequent HRV analysis.

Noise removal techniques exist (such as using band pass filters) toremove low frequency noise such as baseline drift and also attenuatehigh frequency variations without significant distortion of the QRScomplex. The presence of abrupt baseline shift and other artifacts canresult in peaks being wrongly detected as QRS complexes. Since theseartifacts may lie within the same frequency range as the QRS complex,they may be difficult to eliminate. Thus, in step 610 baseline wanderingis removed from the unfiltered trimmed ECG data and in step 612, the DCoffset is removed.

Frequency components of the QRS complex typically lie between a range of10 and 25 Hz. In step 614, the data from step 612 is processed using aband pass filter with an operating frequency range of 5 to about 28 Hz.It will thus be appreciated that the band pass filter facilitateslocation of QRS complex by enhancing the QRS complex inside theunfiltered trimmed ECG data from step 612 and to suppress high frequencyvariations. A bandpass frequency range, that is successful ineliminating baseline drift and magnifying the QRS complex withoutsignificantly distorting the signal and increasing the chance of falsedetections, is applied.

In step 616, a de-noised ECG signal is obtained which is used forfurther processing to detect QRS and calculate HRV measures. In step618, the de-noised ECG signal waveform is displayed for instance in thescreen 432 (see FIG. 4).

FIG. 7 shows a flow chart 700, in accordance with embodiments of theinvention, implemented by the beat detection and post processing module414 of FIG. 4.

The flow chart 700 begins with step 702 with the beat detection and postprocessing module 414 having a de-noised ECG signal.

In summary, the objective of steps 704 to 726 is to detect the locationof the QRS complexes, which allows us the calculation of RR intervals.The location, magnitude and shape of the QRS complex as well as theduration between adjacent complexes allows sifting out ectopic beats andother non-sinus rhythm which is to be excluded from the HRV analysis. Inthis manner, reliable heart rate variability data can be extracted froman ECG signal from a patient.

In steps 706 to 714, a maximum peak data value first occurring in thefiltered ECG signal is located. An upper amplitude threshold and a loweramplitude threshold from the located maximum peak value are determined.A peak value and minimum values on either side of the peak value arelocated. In embodiments of the invention, either side refers to the leftand right sides of the peak value. The conditions of whether the peakvalue is above the upper amplitude threshold, while the minimum valuesare below the lower amplitude threshold are met is checked. If theconditions are met, the location of the peak value is denoted as an Rposition. The location of the minimum value occurring closest on theleft side of the R position is denoted as a Q position, and the locationof the minimum value occurring closest on the right hand side of the Rposition is denoted as an S position. The location of a QRS peak withinthe filtered ECG signal is thus determined.

Further detail on steps 704 to 726 is provided as follows.

In step 704, a modified threshold-plus-derivative method is used as ithas found to be effective and robust in the presence of noise. Themodified algorithm works as follows.

In step 706, a maximum peak data (ref_peak) value is found, given a 1Darray of ECG sample points x(n), within the first few seconds ofde-noised ECG data. In step 708, upper and lower amplitude thresholdsare found.

In embodiments of the invention, the upper and lower amplitudethresholds (T_(upper) and T_(lower)) are set after finding the maximumvalue (ref_peak) within the first few seconds of data. The thresholdsare defined as:T _(upper)=ref_peak+0.4*ref_peakT _(lower)=ref_peak−0.35*ref_peak

In step 710, it is determined whether the ECG sample points cross theupper and lower amplitude thresholds (T_(upper) and T_(lower)). The flowchart 700 does not proceed to step 712 if the ECG sample points do notpass this criteria. The use of the upper and lower amplitude thresholds(T_(upper) and T_(lower)) for QRS complex detection ensures that largepeaks due to noise (e.g. as a result of electrode placement or motionartifacts) are not detected as QRS complexes.

Step 712 occurs if the ECG sample points cross the upper and loweramplitude thresholds (T_(upper) and T_(lower)). In step 712, it isdetermined whether the sample points that pass the criteria check atstep 710 can be considered as a QRS peak. A QRS peak is said to occur atthe point i if the following further conditions are met,

-   -   x(i) lies between T_(upper) and T_(lower);    -   x(i+1)−x(i)<0;    -   x(i)−x(i−1)>0;        where the R-peak is the point with maximum value.

If the further conditions above are met, the points corresponding to theQ and S waves are then determined by locating the nearest local minimumwithin a window on either side of the R-peak. The exact locations of theQ, R and S positions are then saved in step 714. Otherwise (i.e. if thefurther conditions above are not met), the flow chart 700 returns tostep 710. The positions of other QRS peaks within the filtered ECGsignal may be located by iterating the process of steps 710 and 712,i.e. locating another peak value and locating other minimum values oneither side of the another peak value. When another peak value is abovethe upper amplitude threshold while the other minimum values are bothbelow the lower threshold, the location of the peak value is denoted asan R position. The location of the minimum value occurring closest onthe left side of the R position is denoted as a Q position and thelocation of the minimum value occurring closest on the right side of theR position is denoted as an S position. In this manner, the location ofanother QRS peak is determined. All positions of QRS peaks are thenstored in step 714.

Besides noise, ectopic beats and other outliers (due to exercise, muscleor other artifacts) have to be identified because they can perturb theRR interval sequence.

Ectopic beats are generated when autonomic modulation of the sinoatrialnode is temporarily lost, initiating a premature contraction of theatria or ventricles, occurring both in normal subjects as well aspatients with heart disease. Generally, most such ectopics aremanifested with a wide QRS complex.

Steps 716 to 726 are used to removing outliers from the sequence ofinformation within the RR interval. The process involves finding amedian value and a standard deviation value for the RR interval. Atolerance factor based on the standard deviation value is calculated. Aportion of information that lies within the RR interval spanning eitherside of the median value by the tolerance factor is retained. Heart ratevariability data may be obtained from the retained portion ofinformation and the remaining portion of the information from thesequence of information is discarded.

Further detail on steps 716 to 726 is provided as follows.

In step 716, non-sinus beats are isolated. Beats adjacent to thenon-sinus beats are removed to produce a clean QRS peak in step 718.

The RR interval sequence is then generated in step 720 based on normalbeats. Once this is done, the locations of beats corresponding to sinusrhythm are stored in an array for the next stage of processing. Usingthe detected peaks, the RR intervals correspond to the distance betweensuccessive QRS peaks. The calculated intervals are stored in an arrayfor post-processing. Although noise, artifacts and isolated abnormalbeats are already been filtered, the beats can result in very short orvery long RR intervals either due to compensatory pauses or by virtue ofremoval of some beats. Hence, the sequence may contain outliers.

To automatically identify these outliers, the statistical properties ofthe sequence are applied onto the RR interval sequence in step 720.

In step 722, a RRI limit is calculated as follows.

-   1. Find the median and standard deviation for the RR interval    sequence-   2. Calculate a tolerance factor based on the standard deviation (s)-   3. Search for any intervals lying more than Ms away from the median    interval, where M is the tolerance factor. Outliers exist within the    intervals lying more than Ms away from the median interval.-   4. Separate these outliers, which occurs in step 724

In step 724, a tolerance factor is calculated based on the spread of thevalues. The tolerance factor this is used to separate the outliers, thustackling both noisy as well as normal data. Therefore, sinus RRIsequences which are noise-free and ectopic-free are generated in step726 before computing HRV parameters.

To summarize FIGS. 6 and 7, extracting the heart rate variability data,in embodiments of the invention, comprises filtering the ECG signal toremove noise and artifacts, locating a QRS complex within the filteredECG signal; finding a RR interval between successive QRS peaks of theQRS complex; and processing the sequence of information within the RRinterval to obtain the heart rate variability data.

FIG. 8 shows a flow chart 800, in accordance with embodiments of theinvention, implemented by the HRV parameter calculation module 416 ofFIG. 4.

The flow chart 800 begins with step 802 with the HRV parametercalculation module 416 having sinus RR interval (sinus RRI) sequences.

Three categories of HRV measures, time domain data, frequency domaindata and geometric domain data are calculated from the sinus RRIsequences.

In step 804, time domain data such as mean of RR intervals (mean RR),standard deviation of RR intervals (STD), mean of the instantaneousheart rate (mean HR), standard deviation of the instantaneous heart rate(STD_HR), root mean square of differences between adjacent RR intervals(RMSSD), number of consecutive RR intervals differing by more than 50 ms(NN50), and percentage of consecutive RR intervals differing by morethan 50 ms (pNN50) is calculated. Time domain analysis is based onstatistical parameters (primarily based on standard deviation)calculated from the RR intervals over time for both short-term (lessthan 5 mins) as well as long-term recordings (more than 24 h).

The meaning of each of the terms: mean RR, STD, mean HR, STD_HR, RMSSD,NN50 and pNN50 is provided below.

Mean RR (or aRR) is the average width of the RR interval measured inmilliseconds or seconds. This gives a general idea of the heart rate andcan be calculated for both long-term as well as short-term recordings.

STD (or SDNN) is the standard deviation of all RR intervals in the dataset [21], giving a general idea of the spread of the values. STD issuitable for both short-term as well as long-term recordings.

Mean HR is the mean of the instantaneous heart rate.

STD_HR is the standard deviation of the instantaneous heart rate.

RMSSD (or r-MSSD or SDSD) is found by taking the square root of the meanof the sum of the squares of differences between successive heartperiods in a 24-hour interval. It is an index of the variation in RRinterval length. RMSSD is not a sensitive measure of variation over longperiods of time but it is particularly sensitive to misclassified orbeat-labeling errors like retaining premature ventricular contractions.Among the time domain variables, this is the most sensitive to vagalinfluences, although it is unable to determine the sympathetic andparasympathetic contributions.

NN50 (or RR=50) is the total number of times in 24 hours that thedifference between 2 successive RR intervals exceeds 50 ms. It is themost sensitive of all measures to mislabeled beats and occurrences ofpremature ventricular or atrial contractions will rapidly increase theRR50 count. It is also highly sensitive to longer variations of theheart periods of normal sinus rhythm.

pNN50 (or % RR50) is the percentage of absolute differences betweennormal RR intervals that exceed 50 ms, normalized by the average heartrate.

In step 806, frequency domain data such as: power in very low frequencyrange (<=0.04 Hz) (VLF), power in low frequency range (0.04 to 0.15 Hz)(LF), power in high frequency range (0.15 to 0.4 Hz) (HF) being an indexof vagal activity, total power which is estimated from the variance ofNN intervals in the segment and is measured in ms² (TP), ratio of LFpower to HF power (LF/HF), LF power in normalized units: LF/TP−VLF)×100(LFnorm), and HF power in normalized units: HF/TP−VLF)×100 (HFnorm) iscalculated. Spectral analysis is a sensitive, quantitative method forevaluating HRV in the frequency domain. The analysis is done bytransforming the time series to the frequency domain and finding thepower spectrum. The distribution of spectral energy in various bands isquantified and used as an index of variability. This distribution ofenergy reflects the contribution of the sympathetic and parasympatheticarms of the autonomic nervous system.

In step 808, geometric domain data such as: total number of all RRintervals divided by height of histogram of intervals (HRV Index) andbase width of triangle fit into RR histogram using least squares method(TINN) is obtained.

The meaning of the terms: HRV Index and TINN is provided below.

HRV index (or HRV triangular index or RR triangular index) is obtainedafter the RR interval sequence is converted to a sample densitydistribution. The triangular index is the integral of the densitydistribution, i.e., the number of all RR intervals divided by themaximum of the density distribution.

TINN, the triangular interpolation of RR interval histogram, is thebaseline width of the sample density distribution measured as a base ofa triangle approximating the RR interval distribution.

In step 810, the above 16 HRV parameters (Mean RR, STD, Mean HR, STD_HR,RMSSD, NN50, pNN50, VLF, LF, HF, TP, LF/HF, LFnorm, HFnorm, HRV Indexand TINN) are combined and sent to the analysis block 406 (see FIG. 4)for classifier training (i.e. training of the artificial neural networkwithin the analysis block 406) and patient outcome prediction.

FIG. 9 shows a block diagram representation of how data flows in theanalysis block 406 of FIG. 4.

The analysis block 406 is first configured to be trained (represented byreference numeral 902) using training data and subsequently the trainedanalysis block 406 is tested using testing data (represented byreference numeral 904).

In step 906, a training data set is constructed in which each patient isrepresented as a feature vector of HRV parameters, clinical information(like age, gender, ethnicity) and vital signs.

In step 908, the training data set represented as feature vectors isfurther processed with feature selection and/or extraction algorithmsfor reducing feature dimensionality so as to remove redundantinformation.

Besides discriminatory features, the selection of a classifier plays akey role in building an efficient prediction system. Judging aclassifier usually depends on evaluating its generalization ability thatrefers to the classifier's performance in categorizing unseen patterns.Since the same classifier may have various performances on differentapplications, the needs of the application should be analyzed beforechoosing a proper classifier. In order to predict the outcomes forunseen patients, the classifier should be trained with training samplesprior to doing categorization on testing samples. Therefore, in step 910a classification model, suitable for the application at hand, is learntafter choosing proper pattern representations in step 908.

In step 912, testing data from a patient is represented as a combinedfeature vector of HRV measures, clinical information and vital signs.

In step 914, feature selection and/or extraction algorithms are appliedto the testing data from a patient represented as the combined featurevector for extracting discriminatory information.

In step 916, the extracted discriminatory information is processed usingthe classification model selected in step 910. The output 918 from step916 is a label of the testing data, giving a prediction on the patientoutcome.

FIG. 10 shows a flow chart 1000 illustrating a system, in accordancewith embodiments of the invention, utilizing wireless technology.

The flow chart 1000 begins with step 1002, where a patient survivabilityprediction system, has data on clinical information, HRV parameters,vital signs and a patient survivability risk prediction.

In step 1004, wireless technologies such as GPRS or WAP are used toestablish a network infrastructure between the patient survivabilityprediction system described in step 1002 and peripheral systems such asa hospital server, other handheld devices or a emergency centre server.In steps 1006, 1008 and 1010, the data of the patient survivabilityprediction system is transmitted to the hospital server, the handhelddevice and the emergency centre server. The steps 1006, 1008 and 1010allows clinicians to receive and analyze patients' condition inreal-time and remotely.

FIG. 11 summarizes raw ECG data characteristics of 100 patients chosenfor analysis, including 40 cases of death and 60 cases of survival. Thedata set comprised 63 male and 37 female patients between the ages of 25and 92 years. Vital signs and patient outcomes were obtained fromhospital records, including information such as patient demographics(age, race, gender) and priority code.

These 100 patients were acquired from critically ill patients attendedat the Department of Emergency Medicine (DEM), Singapore GeneralHospital (SGH). “Critically ill” refers to patients triaged in the mostsevere categories P1 or P2 at the DEM. These include trauma andnon-trauma patients who underwent ECG monitoring. ECG signals wereacquired using LIFEPAK 12 defibrillator/monitor, downloaded using theCODE-STAT Suite and matched with the patients' hospital records. Caseswere included for review if they contained more than 70% sinus rhythmand excluded if there were large segments of non-sinus rhythm (atrialand ventricular arrhythmias).

The raw ECG data shown in FIG. 11 has to be pre-processed to obtainreliable HRV measures. FIG. 12 shows a flow chart 1200, in accordancewith embodiments of the invention, illustrating how an ECG signal ispre-processed to calculate HRV parameters.

In step 1202, raw ECG data 1210 is processed to reduce the effects ofnoise and artifacts using a 5-28 Hz band-pass filter. This frequencyrange is found to enhance the QRS complex against the background noisefor easy peak detection.

In step 1204, a modified threshold-plus-derivative method is implementedto detect the QRS complexes.

In step 1206, all ectopics and other non-sinus beats are excluded.

In step 1208, the RR intervals are calculated based on the sinus rhythm.Cases are included for review if they contain more than 70% sinus rhythm(measured as number of sinus beats detected/total number of detectedbeats) and excluded if they contain sustained arrhythmias or largesegments of noise/artifact. The resulting beat-to-beat (RR) intervalsequences 1210 are used for calculating various HRV measures.

In embodiments of the invention, steps 1202 to 1208 can use themethodology as described with reference to FIGS. 6 and 7. Thus, nofurther elaboration is provided on steps 1202 to 1208.

Classification of the Artificial Neural Network

In training the artificial neural network used in embodiments of theinvention, the first parameter, the second parameter or a combination ofthe first parameter and the second parameter may be classified asfeature vectors of the patient health data. The artificial neuralnetwork is then trained with the feature vectors. As one objective ofthe artificial neural network is to predict mortality, the artificialneural network will be implemented to solve a two-class classificationproblem (the patient outcome is either death or survival).

In embodiments of the invention, various training algorithms may be usedto train the artificial neural network (200, 300) and determine theoptimal hidden layer weights (see description in respect of FIGS. 2 and3).

Levenberg-Marquardt Algorithm

For instance, training of the artificial neural network (200, 300) maybe based on back-propagation learning. The Levenberg-Marquardt algorithmmay be used to perform the back-propagation learning.

Extreme Learning Machine (ELM)

An extreme learning machine architecture may be used to trainembodiments of the invention where a SLFN is used (such as the one shownin FIG. 2). Compared with conventional gradient-based learningapproaches, ELM has a fast learning process and meanwhile retains goodgeneralization ability. The extreme learning machine has the advantageof improving training speed by eliminating the need to tune all theparameters of the artificial neural network. The extreme learningmachine may be implemented for SLFN with either additive neurons orradial basis function (RBF) kernels.

In an extreme learning machine architecture, the associated weight andbiases of the at least one input of each artificial neuron of theartificial neural network is initialized through random selection. Theoutput weights of each artificial neuron may be determined by findingthe least square solution.

Given a training set consisting of N samplesL={(x _(j) ,t _(j))|x _(j) εR ^(n) ,t _(j) εR ^(m) , j=1,2, . . .,N}  (1)where x_(j) is a p×1 input vector and t_(j) is an q×1 target vector, anSLFN with N hidden nodes is formulated as

$\begin{matrix}{{{f_{\overset{\sim}{N}}\left( x_{j} \right)} = {{\sum\limits_{i = 1}^{\overset{\sim}{N}}\;{\beta_{i}{g\left( {{w_{i} \cdot x_{j}} + b_{i}} \right)}}} = {{t_{j}\mspace{14mu} j} = 1}}},\ldots\mspace{14mu},N} & (2)\end{matrix}$wherein x_(j) is an input vector to an input of one of the plurality ofartificial neurons for j=1, 2, . . . , N input vectors; w_(i) is theassociated weight of the input of the artificial neuron receiving thex_(j) input vector; g(w_(i)·x_(j)+b_(i)) is an output of the artificialneuron receiving the x_(j) input vector . . . for i=1, 2, . . . , Nartificial neurons; β_(i) is the output weight vector that associates ani^(th) hidden neuron with a respective output neuron; and b_(i) is thebias for the i^(th) hidden neuron. The prediction on the survivabilityof the patient is derived from the equation (2) above.

A compact format of equation (2) can be written asH{circumflex over (β)}=T  (3)where H(w₁, . . . , w_(Ñ), b₁, . . . , b_(Ñ), x₁, . . . , x_(N)) ishidden layer output matrix of the network, h_(ji)=g(w_(i)·x_(j)+b_(i))is the output of ith hidden neuron with respect to x_(j, i)=1, 2, . . ., Ñ and j=1, 2, . . . , N; {circumflex over (β)}=[β₁, . . . , β_(Ñ)]^(T)and T=[t₁, . . . , t_(N)]^(T) are output weight matrix and targetmatrix, respectively. To obtain small non-zero training error, randomvalues can be assigned for parameters w_(i) and b_(i) and thus thesystem becomes linear so that the output weights can be estimated asβ=H^(†)T, where H^(†) is the Moore-Penrose generalized inverse of thehidden layer output matrix H.

$\begin{matrix}{{H\left( {w_{1},\ldots\mspace{14mu},w_{\overset{\sim}{N}},b_{1},\ldots\mspace{14mu},b_{\overset{\sim}{N}},x_{1},\ldots\mspace{14mu},x_{N}} \right)} = \begin{bmatrix}{g\left( {{w_{1} \cdot x_{1}} + b_{1}} \right)} & \ldots & {g\left( {{w_{\overset{\sim}{N}} \cdot x_{1}} + b_{\overset{\sim}{N}}} \right)} \\\vdots & \ldots & \vdots \\{g\left( {{w_{1} \cdot x_{N}} + b_{1}} \right)} & \ldots & {g\left( {{w_{\overset{\sim}{N}} \cdot x_{N}} + b_{\overset{\sim}{N}}} \right)}\end{bmatrix}_{N \times \overset{\sim}{N}}} & (4)\end{matrix}$In general, the ELM algorithm can be summarized as follows:

-   1) Generate parameters w_(i) and b_(i) for i=1, . . . , Ñ,-   2) Calculate the hidden layer output matrix H,-   3) Calculate the output weight using β=H^(†)T.    Support Vector Machine (SVM)

Another training algorithm is basing the artificial neural network onsupport vector machine architecture. A support vector machine is alearning machine designed for binary classification. In the supportvector machine, input vectors are non-linearly mapped to a veryhigh-dimensional feature space in which a linear decision surface(hyperplane) is constructed. The surface is chosen such that itseparates input vectors with maximum margin.

The associated weight of the at least one input of each artificialneuron is initialized from a library used by the support vector machine.An example of a suitable library would be the LIBSVM software package byChang et al.

Consider a set of linearly separable features (x₁,y₁), . . . ,(x_(N),y_(N)) are given as training data, where x_(i)εX,y_(i)ε{±1} witha hyperplane <w, x>+b=0. The set of vectors is said to be optimallyseparated by the hyperplane if it is separated without errors and themargin is maximal. A canonical hyperplane has the constraint forparameters w and b: min_(xi) y_(i)((w, x_(i))+b)=1. A separatinghyperplane in canonical form must satisfy the constraints:y _(i)(<w,x _(i) >+b)≧1, i=1, . . . ,N  (5)

Quadratic programming is used for solving the constraint optimizationproblem in order to find the optimal hyperplane. The optimizationcriterion is the width of the margin between the class. Then for a newpattern x, the hyperplane decision function can be written as

$\begin{matrix}{{f(x)} = {{sgn}\left( {{\sum\limits_{i = 1}^{N}\;{\alpha_{i}y_{i}\;\left\langle {x,x_{i}} \right\rangle}} + b} \right)}} & (6)\end{matrix}$Since most real-world data is nonlinearly distributed, a kernel trickhas been used to extend the classifier to be nonlinear, in which kernelfunctions are used to replace the simple dot product. The weight vectorthen becomes an expansion in the feature space, and we obtain thedecision function of the support vector machine may be given by

$\begin{matrix}{{f(x)} = {{sgn}\left( {{\sum\limits_{i = 1}^{N}\;{\alpha_{i}y_{i}{k\left( {x,x_{i}} \right)}}} + b} \right)}} & (7)\end{matrix}$wherein sgn( ) is a sign function; (x;x_(i)) is set of feature vector;k(x;x_(i)) is a kernel matrix constructed by x and x_(i);y_(i) is 1 or−1; which is the label of feature vector x_(i); α_(i) and b areparameters used to define an optimal decision hyperplane so that themargin between two classes of patterns can be maximized in the featurespace.

Three kernels may be used to provide diversified solutions, they arelinear kernel k(x_(i), x_(j))=x_(i)·x_(j), sigmoid kernel k(x_(i),x_(j))=tan h(αx_(i)·x_(j)+γ), and radial basis function (RBF) kernelk(x_(i), x_(j))=exp(−∥x_(i)−x_(j)∥²/2σ²) where σ is the width of RBFfunction.

Segment Based Method

When measuring ECG signals from patients, the length of ECG signalvaries from one patient to another, which will affect the calculation ofHRV measures.

To avoid possible effects of length variation, segments of identicallength of ECG signals are extracted for all patients. Since raw ECG datacontains non-sinus beats and noise, extraction is done on the RRinterval sequences. FIG. 13 shows how the extraction is performed. InFIG. 13, a sequence of information (1302, 1304 and 1306) within an RRinterval (1308, 1310 and 1312) is partitioned into segments 1314, inaccordance with embodiments of the invention.

In embodiments of the invention, the sequence of information (1302, 1304and 1306) within the RR interval (1308, 1310 and 1312) may bepartitioned into non-overlapping segments 1314. The non-overlappingsegments 1314 may be used to train an artificial neural network.

In other embodiments of the invention, a length of signal within the RRinterval (1308, 1310 and 1312) of each of the filtered ECG signal may beextracted. The length of signal may be partitioned into non-overlappingsegments 1314; and at least one of the non-overlapping segments 1314 maybe selected to train the artificial neural network.

In embodiments of the invention, each of the non-overlapping segments1314 may be of substantially equal length. In embodiments of theinvention, the non-overlapping segments 1314 may have a fixed length. Inembodiments of the invention, each of the non-overlapping segments 1314may be of unequal length. In embodiments of the invention, thenon-overlapping segments 1314 may be of an adjustable length.

Extraction starts from the signal end 1306 as this portion of recordingcorrelates better with the patient outcome than any other segments inthe original sequence. The entire sequence (1308, 1310 and 1312) and theextracted portion (1302, 1304 and 1306) as “global” signal and “local”signal, respectively.

High prediction accuracy may not be achieved with only N (number ofpatients) feature vectors. The local sequence (1302, 1304 and 1306) maybe further partitioned into several non-overlapped segments 1318, 1320and 1322 of fixed length and the prediction of the patient outcome isgiven by majority voting using the patient's corresponding segments.

Firstly, an ensemble of classifiers with M segments of the same patientare combined to improve the overall predictive performance. Since theoutputs of a predictor can be either class labels or class-specificcontinuous values (the degrees of support given to those classes), thereare two types of combination rules. The patient outcome is coded aseither 0 or 1, thus the label-based strategy such as majority voting canbe used as the combining method. This rule seeks the class that receivesthe highest number of votes and assigns it to the predicted label forthe testing pattern. The details of the segment based prediction methodis elaborated as follows, noting that while ECG data is shown in FIG.13, the segment based prediction method is applicable to other 1-Dbiomedical signals such as electroencephalography (EEG).

Suppose a data set L, {(x_(m),y_(n)), n=1, . . . , N, m=1, . . . , M},consists of N patients and each local sequence is divided into Msegments. Assume that if {circumflex over (x)} is the test data, ŷ ispredicted by φ({circumflex over (x)}, L). Because M segments are used,we have a set of M predictive labels for {circumflex over (x)}. Theobjective is to better predict ŷ using M predictors instead of a singleone. As a two-class problem is being considered, φ({circumflex over(x)}, L) predicts a series of class labels ω_(j)ε{0, 1} where j=1, 2,and the prediction of the m^(th) classifier (constructed on m^(th)segment) is D_(m,j) whose value is assigned to 1 if the m^(th)classifier chooses class ω_(j), and 0 otherwise. Then the decision on{circumflex over (x)} is defined as

$\begin{matrix}{\hat{y} = {\overset{2}{\max\limits_{j = 1}}\mspace{11mu}{\sum\limits_{m = 1}^{M}\; D_{m,j}}}} & (8)\end{matrix}$where the output ŷ is the value with highest number of votes. Inapplications where there are J classes, i.e., j=1, . . . , J, thepredictive label is given by max_(j=1) ^(J)Σ_(m-1) ^(M)D_(m,j)

Thus far, a total segment (TS) method approach is discussed as all Msegments are used for decision making. The complete TS algorithm isprovided below.

TS Algorithm

Inputs

-   -   ECG signals of N patients, S₁ . . . , S_(N).    -   Hospital records including vital signs and patient outcomes y₁,        . . . , y_(N).    -   Number of iterations K and number of total segments M.        Calculation of HRV Measures

-   1. Do pre-processing on the original ECG signals such as filtering,    QRS detection, non-sinus beat removal, etc.

-   2. Extract “local” RR interval signals to obtain sequences S′₁ . . .    S′_(N).

-   3. Partition S′_(N) into M non-overlapped segments and calculate HRV    measures z_(n) ^(m) where n=1, . . . , N and m=1, . . . , M.

-   4. Construct feature vectors x_(n) ^(m) with z_(n) ^(m) and vital    signs, where m=1, . . . , M.    Prediction of ACP Event or Mortality

For k=1, K

-   a) Partition the data set by randomly selecting N_(trn) patients    into training set and the rest of N_(tst) patients into testing set.    Since each patient is represented by M feature vectors, there are    N_(trn)M samples in the training set and N_(tst)M samples in the    testing set.-   b) Train classifier with N_(trn)M feature vectors and predict labels    for N_(tst)M samples in the testing set. Therefore, each testing    patient receives M predicted outcomes. Applying majority voting    rule, final predictive results for all testing patients are obtained    using equation (8).-   c) Calculate accuracy, sensitivity, and specificity from the    predicted labels and their corresponding real labels.    End for    Outputs    -   Calculate averaged results of K iterations.    -   Store, display, and analyze the final results.

Instead of selecting all segments, a selective segment (SS) method canbe used. The SS method selects only some of the segments.

The rationale behind the SS method is to select some “optimal” segmentsto minimize the intra-class difference where Euclidean distance [6] isemployed as the selection criteria. Specifically, within the featureset, the class center is determined and the distances between each of Msegments of any patient and the center are calculated. Let M^(I) be thenumber of selected segments, then M^(I) segments will be retained, whichare closer to the corresponding class center than the discardedsegments. As a result, the size of data set has been reduced from N×M toN×M^(I). Since the selecting operation is supervised (the classinformation is used), the selection of segments can be considered as apre-processing for the original data set. The complete SS algorithm isprovided below.

SS Algorithm

Inputs

-   -   ECG signals S₁ . . . , S_(N).    -   Vital signs and patient outcomes y₁, . . . , y_(N).    -   Number of iterations K, number of total segments M, and number        of selected segments M′.        Calculation of HRV Measures

-   1. Do steps 1-3 in TS algorithm to obtain M segments for each    patient.

-   2. Calculate class centers as

$C_{0} = {\frac{1}{N^{0}}{\sum\limits_{x_{i} \in w_{0}}^{\;}\; x_{i}}}$and$C_{1} = {\frac{1}{N^{1}}{\sum\limits_{x_{i} \in w_{1}}^{\;}\; x_{i}}}$

-    where N^(i) is the number of samples in class ω_(i) for i=0, 1.    3. Calculate Euclidean distances d_(n) ^(m) between N and M segments    and the class centers C₀, C₁.-   4. Sort the distances and select M′ segments that are closer to the    corresponding center than other segments for each patient    individually.-   5. Construct feature vectors x_(n) ^(m′) with z_(n) ^(m′) and vital    signs, where m′=1; . . . , M′.    Prediction of ACP Event or Mortality    For k=1, K    -   Do steps a)-c) in TS algorithm with a data set created by using        M′ selected segments instead of the total M segments.        End for        Outputs    -   Calculate averaged results of K iterations.    -   Store, display, and analyze the final results.

In summary, any of the above methods to classify an artificial neuralnetwork may be used to facilitate a method of predicting thesurvivability of a patient.

FIG. 14 is a flow chart 1400 illustrating a method, according to oneembodiment of the present invention, of predicting the survivability ofa patient.

In step 1402, a first set of parameters relating to heart ratevariability data of a patient is measured.

In step 1404, a second set of parameters relating to vital sign data ofthe patient is measured.

In step 1406, an artificial neural network including a network ofinterconnected nodes is provided, the nodes including a plurality ofartificial neurons. Each artificial neuron has at least one input withan associated weight adjusted by training the artificial neural networkusing an electronic database having a plurality of sets of data. Eachset of data has at least a parameter relating to heart rate variabilitydata and a parameter relating to vital sign data, each set of datafurther having a parameter relating to patient survivability.

In step 1408, the first set of parameters and the second set ofparameters are processed to produce processed data suitable for inputinto the artificial neural network.

In step 1410, the processed data is provided as input into theartificial neural network.

In step 1412, an output is obtained from the artificial neural network,the output providing a prediction on the survivability of the patient.

In embodiments of the invention, the processed data of the first set ofparameters and the processed data of the second set of parameters may berepresented as a feature vector.

In embodiments of the invention, the processed data may be the first setof parameters and the second set of parameters being represented asnormalized data.

In embodiments of the invention, the processed data may be partitionedinto non-overlapping segments, so that the input into the artificialneural network may include sets of one or more of the non-overlappingsegments of processed data. A result may be obtained for each of thesets of one or more of the non-overlapping segments of processed data,so that each of the results may be considered to predict thesurvivability of the patient.

In embodiments of the invention, majority voting may be used todetermine the prediction on the survivability of the patient, themajority voting represented by the function

$\hat{y} = {\overset{2}{\max\limits_{j = 1}}\mspace{11mu}{\sum\limits_{m = 1}^{M}\; D_{m,j}}}$wherein D_(m,j) is an intermediate variable for final decision making,D_(m,j) assigned a value of 1 if a m^(th) classifier chooses class j inthe decision ensemble, and 0 otherwise.

In embodiments of the invention, the result of the artificial neuralnetwork may be coded as a two class label. The method of predicting thesurvivability of a patient may then further include applying alabel-based algorithm to each of the two class label results to decidethe output from the artificial neural network, thereby providing aprediction on the survivability of the patient.

In embodiments of the invention, the heart rate variability data mayinclude time domain data, frequency domain data and geometric domaindata.

FIG. 15 shows a schematic of a patient survivability prediction system1500 in accordance with embodiments of the invention.

The patient survivability prediction system 1500 includes a first input1502 to receive a first set of parameters relating to heart ratevariability data of a patient and a second input 1504 to receive asecond set of parameters relating to vital sign data of the patient.

The patient survivability prediction system 1500 includes a memorymodule 1506 storing instructions to implement an artificial neuralnetwork. The artificial neural network includes a network ofinterconnected nodes, the nodes including a plurality of artificialneurons. Each artificial neuron has at least one input with anassociated weight adjusted by training the artificial neural networkusing an electronic database having a plurality of sets of data. Eachset of data has at least one a parameter relating to heart ratevariability data and a parameter relating to vital sign data. Each setof data further has a parameter relating to patient survivability.

The patient survivability prediction system 1500 further includes aprocessor 1508 to execute the instructions stored in the memory module1506 to perform the functions of the artificial neural network andoutput a prediction on the survivability of the patient based on thefirst set of parameters and the second set of parameters. A display 1510displays the prediction on the survivability of the patient.

In embodiments of the invention, the patient survivability predictionsystem 1500 includes a port 1512 to receive the first set of parametersfrom the first input 1502 and the second set of parameters from thesecond input 1504.

FIG. 16 shows a schematic of a patient survivability prediction system1600 in accordance with embodiments of the invention.

The patient survivability prediction system 1600 shares similarcomponents with the patient survivability prediction system 1500 of FIG.15. The main contrast between the patient survivability predictionsystem 1600 and the patient survivability prediction system 1500 of FIG.15 is that the patient survivability prediction system 1600 does not usea single port to receive the first set of parameters from the firstinput 1502 and the second set of parameters from the second input 1504.Rather, the patient survivability prediction system 1600 has a firstport 1602 to receive the first set of parameters from the first input1502 and a second port 1604 to receive the second set of parameters fromthe second input 1504.

FIG. 17 shows pictures of a patient survivability prediction system 1700in accordance with embodiments of the invention.

In FIG. 17, the patient survivability prediction system has ECG sensors1702 and a blood pressure sensor 1704. The artificial neural networkused to predict patient survivability is implemented in a laptop 1706.

FIGS. 18 to 21 show snap shots of the output of the patientsurvivability prediction system as shown in the laptop 1706 screen.

FIG. 18 shows the result of processing raw ECG data 1802 to producefiltered ECG data 1804.

FIG. 19 shows various signal graphs that the patient survivabilityprediction system 1700 is able to display.

FIG. 20 shows the prediction results of two different patients, where inone case (2102), cardiac arrest is predicted to not occur within 72hours. In the other case (2104), cardiac arrest is predicted to occurwithin 72 hours.

FIG. 21 shows a flow chart 2150 illustrating a method, according to oneembodiment of the present invention, used to predict the survivabilityof a patient.

The method includes six steps, 2152, 2154, 2156, 2158, 2160 and 2162.

In step 2152, a first set of parameters relating to heart ratevariability data of a patient is measured.

In step 2154, a second set of parameters relating to vital sign data ofthe patient is measured.

In step 2156, a third set of parameters relating to patientcharacteristics is obtained.

In step 2158, the first set of parameters, the second set of parametersand the third set of parameters are provided as sets of normalized datavalues, where required, to a scoring model implemented in an electronicdatabase. The scoring model has a respective category associated to eachparameter of the first set of parameters, the second set of parametersand the third set of parameters, each category having a plurality ofpre-defined value ranges, each of the plurality of value ranges having apre-defined score.

In step 2160, a score for each parameter of the first set of parameters,the second set of parameters and the third set of parameters isdetermined. The score is determined by assigning the sets of normalizeddata (from step 2158) to respective pre-defined value ranges,encompassing the sets of normalized data values, of the plurality ofvalue ranges of the category associated to the respective parameter ofthe first set of parameters, the second set of parameters and the thirdset of parameters.

In step 2162, a total score, being a summation of the score (see step2160) for each parameter of the first set of parameters, the second setof parameters and the third set of parameters, is obtained. The totalscore provides an indication on the survivability of the patient.

The method illustrated in FIG. 21 may be implemented in accordance tothe example that follows, the example relating to predicting cardiacarrest in a patient within 72 hrs of assessment.

When a patient is delivered to a triage area for assessment, thepatient's characteristics (such as age), vital signs (such as GCS,temperature, pulse rate, respiratory rate, SBP, DBP, SpO2 and painscore) and HRV parameters (time, frequency and geometric domain) will berecorded and analyzed by a patient survivability prediction system inaccordance to an embodiment of the invention. In this embodiment, themeasured HRV parameters become a first set of parameters, while themeasured vital sign data form a second set of parameters. The patientcharacteristics form a third set of parameters, which may also beobtained from the patient's hospital records. It will be appreciatedthat further patient health data may also be recorded by the patientsurvivability prediction system.

The patient survivability prediction system may have an electronicdatabase in which a scoring model is implemented. The scoring model maybe based on a mathematical model which may be based on logisticregression, such as univariate analysis. In one embodiment, the logisticregression mathematical model may be used, for example, on data fromsamples of cardiovascular (CVS) and non-cardiovascular (non-CVS)patients. The logistic regression mathematical model may be fittedseparately with a combination of demographic parameters (age), vitalsigns and HRV parameters for the CVS and non-CVS patients. Theprediction performance may be investigated through Receiver OperatingCharacteristic (ROC) analysis as well as Sensitivity, Specificity,Positive Predictive Value (PPV) and Negative Predictive Value (NPV).Table 1 below summarizes the organization of first set of parameters,the second set of parameters and the third set of parameters inside ascoring model, according to one embodiment of the invention.

TABLE 1 Model based scoring scheme for demographic, vital sign and HRVparameters. Parameter & respective range of values Score Age <40 1 40-492 50-59 2 60-69 3 70-79 4 >=80 4 GCS <=5 6  6-10 4 11-14 3 15 0Temperature <36.5 5 36.5-37.4 0 >37.4 4 Pulse rate <60 4 60-99 1 100-1294 >=130 5 Respiratory rate <10 6 10-16 3 >16 4 SBP <90 6  90-120 2 >1205 DBP <60 4 60-95 2 >95 3 SPO2 <95 5 >=95 0 Pain score 0 0 1-5 3  6-10 4aRR(s) <0.73 0 0.73-0.95 0 >0.95 3 STD(s) <0.04 0 0.04-0.08 0 >0.08 3avHR(bpm) <63.46 0 63.46-83.24 0 >83.24 3 sdHR(bpm) <3.84 0 3.84-6.360 >6.36 3 RMSSD <0.02 0 0.02-0.07 0 >0.07 3 nn50 (count) <3.34 0 3.34-39.64 0 >39.64 3 pnn50 (%) <17.43 3 >=17.43 1 RR triangular index<3.20 5 >=3.20 3 TINN (ms) <0.18 3 0.18-0.33 0 >0.33 0 LS-VLF power(ms2) <0.15 3 >=0.15 0 LS-LF power (ms2) <0.12 3 >=0.12 0 LS-HF power(ms2) <0.08 2 0.08-0.20 3 >0.20 4 LS-total power (ms2) <0.46 3 >=0.46 0LS-LF power (nu) <41.91 3 41.91-70.76 0 >70.76 0 LS-HF power (nu) <29.240 29.24-58.09 0 >58.09 3 LS-LF/HF ratio <0.62 3 0.62-2.54 0 >2.54 0

As shown in table 1, the scoring model has a plurality of categories(Age, GCS, Temperature, Pulse rate, . . . , LS-LF/HF ratio), with eachcategory having a plurality of pre-defined value ranges (for instance:the category “age” has a range of values <40, 40-49, . . . , >=80). Eachof the plurality of pre-defined value ranges has a pre-defined score(for instance: for the category “age”, the range of values <40, 40-49, .. . , >=80 have scores 1, 2, . . . and 4 respectively).

Each of the categories is associated to a respective parameter of thefirst set of parameters, the second set of parameters and the third setof parameters. For instance, the categories “aRR(s)”, “STD(s)”, . . .and “LS-LF/HF ratio” are HRV parameters and are therefore, in thisembodiment, associated with the first set of parameters. The “aRR, STD,. . . and LS-LF/HF ratio” parameters of the first set of parameters willbe associated with the corresponding “aRR(s), STD(s), . . . and LS-LF/HFratio” categories of the scoring model shown in table 1.

In table 1, both the predefined value ranges and their respective scorevalues for the category “age” are derived, for example, from samples ofCVS and non-CVS patients to group variables. Both the predefined valueranges and their respective score values for vital signs (i.e. thecategories “GCS”, “temperature”, “pulse rate”, “respiratory rate”,“SBP”, “DBP”, “SpO2” and “pain score”) are derived according to dataderived from samples of CVS and non-CVS patients. Both the predefinedvalue ranges and their respective score values for the HRV parameters(i.e. the categories “aRR(s)”, “STD(s)”, . . . and “LS-LF/HF ratio”) arebased on ECG studies of a healthy population in Singapore.

As shown in table 1, only required parameters from the first set ofparameters, the second set of parameters and the third set of parametersare normalized. For instance, the parameter “age” from the first set ofparameters and the parameter “temperature” from the second set ofparameters do not need to be normalized as their correspondingcategories in the scoring model are designed to process the actualrecorded values from the patient.

Normalized data, where required, for each parameter of the first set ofparameters, the second set of parameters and the third set of parametersis assigned to its associated category. Further, the normalized data isassigned to the respective value range within the associated category,the normalized data falling within or being encompassed by therespective value range. The purpose of assigning the normalized data toits respective value range within its associated category is todetermine a score, based on the scoring method summarized in table 1, ofthe normalized data. From table 1, it can be observed that a maximumpossible score is 100 and a minimum possible score is 15.

Table 2 below shows a summary of individual scores, obtained from usingthe scoring method summarized in table 1, for each parameter of apatient's demographic, vital sign and HRV parameters.

TABLE 2 Patient demographic, vital sign and HRV parameters Parameter &categories Score Age >=80 4 GCS 11-14 3 Temperature >37.4 4 Pulserate >=130 5 Respiratory rate >16 4 SBP >120 5 DBP >95 3 SPO2 <95 5 Painscore  6-10 4 aRR(s) >0.95 3 STD(s) >0.08 3 avHR(bpm) >83.24 3sdHR(bpm) >6.36 3 RMSSD >0.07 3 nn50 (count) >39.64 3 pnn50 (%) <17.43 3RR triangular index <3.20 5 TINN (ms) <0.18 3 LS-VLF power (ms2) <0.15 3LS-LF power (ms2) <0.12 3 LS-HF power (ms2) >0.20 4 LS-total power (ms2)<0.46 3 LS-LF power (nu) <41.91 3 LS-HF power (nu) >58.09 3 LS-LF/HFratio <0.62 3 Total score 88

As shown in table 2, a total score, being a summation of each score foreach parameter of the first set of parameters, the second set ofparameters and the third set of parameters, is obtained. The total scoreprovides an indication on the survivability of the patient.

Table 3 below summarizes organization of a plurality of risk categoriesinside a scoring model in accordance to an embodiment of the invention.

TABLE 3 Organization of risk categories inside a scoring model Level ofrisk to have cardiac arrest within 72 hrs Score Low 15-40 Moderate 41-60High 61-80 Very high  81-100

Each category (such as low, moderate, high and very high) of theplurality of risk categories has a pre-defined range of values. Thetotal score obtained in table 2 is assigning to the category having thepre-defined range of values encompassing the total score. Thus, for thetotal score “88” from table 2, the patient is assessed to have a “veryhigh” level of risk to have cardiac arrest within 72 hours. In theembodiment shown in table 3, the numerical range of each of plurality ofrisk categories may be determined in an arbitrary manner.

Table 4 shows a summary of results obtained from using the scoringmodel, as shown in FIG. 21B, against actual results of whether cardiacarrest occurred within 72 hours for a sample of 1021 patients.

From table 4, the results obtained by using the scoring model of FIG.21B indicates that for the 1021 patients, 26 (or 2.5% of the samplesize) belonged to the “low” risk category, 661 (or 64.7% of the samplesize) belonged to the “moderate” risk category, 333 (or 32.6% of thesample size) belonged to the “high” risk category, while 1 (or 0.1% ofthe sample size) belonged to the “very high” risk category. Singledecimal place accuracy applies for the percentage values of the samplesizes.

Among the 26 patients of the “low” risk category, cardiac arrest did notoccur. Amongst the 661 patients of the “moderate” risk category, 3.2%suffered cardiac arrest within 72 hours. Amongst the 333 patients of the“high” risk category, 9.0% suffered cardiac arrest within 72 hours. Forthe 1 patient of the “very high” risk category, cardiac arrest occurredwithin 72 hours.

TABLE 4 Assessment of scoring model against actual results Level of riskto have cardiac arrest Patient-at-risk cardiac arrest within 72 hrs (%)within 72 hrs n (%) No Yes Low 26 (2.5) 100.0 0.0 Moderate 661 (64.7)96.8 3.2 High 333 (32.6) 91.0 9.0 Very high  1 (0.1) 0.0 100.0

From table 4, the area under curve (AUC) at a 95% CI (confidenceinterval) of the scores to predict cardiac arrest within 72 hrs rangesfrom 0.633 to 0.769, to have an average accuracy of 0.701.

Experimental Data Set 1

Experiments were conducted where eight vital signs are used to form partof the feature vector for patient outcome prediction. These vital signsare temperature, respiration rate, pulse, systolic blood pressure (SBP),diastolic blood pressure (DBP), oxygen saturation (SpO2), Glasgow ComaScore (GCS), and pain score.

In the data set, each patient was represented as a 24-dimensionalfeature vector and the corresponding outcome coded as either 0 (survivedand discharged) or 1 (died). Among 100 patients, 40 cases died and 60cases survived. Prior to classification, the feature set is transformedinto the interval [−1,1] by performing min-max normalization on theoriginal data. Suppose that min_(A) and max_(A) are the minimum andmaximum values of an attribute vector A=[x₁(i), . . . , x_(N) (i)] whereiε[1, 24] and N is the total number of samples. Min-max normalizationmaps a value v, of A to v′ in the range [min′_(A) and max′_(A)] bycomputing

$\begin{matrix}{v^{\prime} = {{\frac{v - \min_{A}}{\max_{A}{- \min_{A}}}\left( {\max_{A}^{\prime}{- \min_{A}^{\prime}}} \right)} + \min_{A}^{\prime}}} & (9)\end{matrix}$This type of normalization preserves the relationships among theoriginal data values, and therefore facilitates the prediction. Tovalidate embodiments of the patient survivability prediction system, 75patients were randomly selected for training and the rest 25 patientsare used for testing. This partition and classification procedure isrepeated 50 times, and the averaged output values are recorded.

It is known from FIG. 11 that 60 patients belong to class 0 and 40patients are categorized into class 1. As a consequence, randomselection may result in biased training and testing sets, i.e., thesample number of two classes are unbalanced. Alternatively, randompartitioning is done for both classes separately so that 75% samples inclass 0 and 75% samples in class 1 will go into the training set in eachiteration. The validation system is illustrated in FIG. 22. It is seenthat the architecture depicted in FIG. 22 is straight-forward like mostpattern recognition systems, in which data acquisition, featureextraction, and classification are individually implemented.

In practice, the ECG recordings vary widely in length and signalquality.

Therefore, several pre-processing steps are required to ensure qualifiedRR interval sequences. Before computing the HRV measures, the QRSdetection and non-sinus beat detection algorithms were validated againstthe MIT-BIH database. These algorithms were found to perform well withhigh sensitivity (99.8%) and specificity (99.4%) in detecting QRScomplexes and detecting non-sinus beats for ECG signals in the MIT-BIHdatabase.

In the experiments, ELM and SVM are implemented for classification.Therefore, several parameters used in these algorithms should beclarified. In ELM, the number of hidden neurons is assigned as 30. ForSVM, the default settings of the parameters in the LIBSVM package areused. To evaluate the predictive performances, sensitivity andspecificity are calculated in addition to classification accuracy.Serving as widely used statistical measures for binary classification,sensitivity measures the ratio of the number of correctly predictedpositive samples to the actual number of positives, and specificity isthe proportion of negatives which are correctly identified. The decisionwas defined as positive if the patient outcome is death, while negativecase refers to survival. Therefore, the following measures are obtained

-   -   True positive (TP): Death case correctly predicted as death.    -   False positive (FP): Survival case wrongly predicted as death.    -   True negative (TN): Survival case correctly predicted as        survival.    -   False negative (FN): Death case wrongly predicted as survival

Subsequently, sensitivity, specificity, and accuracy was determined andused to evaluate the proposed methods in the experiments.

-   -   Sensitivity=TP/(TP+FN)    -   Specificity=TN/(TN+FP)    -   Accuracy=(TP+TN)/(TP+FP+TN+FN)        In general, high sensitivity, specificity, and accuracy are        desired so that more cases in both classes can be correctly        recognized.        Segment Based Prediction

In the implementation, each segment is set as 250 beats and 9 segmentsper patient are extracted from the original RR interval sequences. Byapplying the voting-based predictive strategy on three selected segments(M′=3), the classification results using vital signs, HRV measures, andcombined features are presented in FIGS. 23, 24 and 25 respectively.

FIGS. 23 and 24 show the prediction results with traditional vital signsand HRV measures, respectively. It can be observed that SVM generallyoutperforms ELM with respect to accuracy and specificity. Both ELM andSVM algorithms achieve comparable performance in terms of sensitivity.Compared with the results based on vital signs, the results based on HRVmeasures give higher accuracy and sensitivity using ELM. Using SVM,results based on vital signs and HRV measures produce similarperformance in terms of accuracy. In addition, sensitivity is increasedand specificity is reduced by replacing vital signs with HRV measures.In general, prediction of mortality with either HRV measures or vitalsigns individually is not satisfactory. By combining the HRV measuresand the vital signs, the best results (Accuracy: 78.32%, Sensitivity:65%, Specificity: 87.2%) are obtained using SVM with linear kernel, ascan be seen from FIG. 25. From these results, it is observed thatcombining the HRV measures and the vital signs can improve theperformance of prediction in general.

Several parameters may affect the final results, particularly the numberof selected segments M′. Hence, prediction results with different valuesof the parameter M′ are investigated in the following. When M′=M, theentire collection of segments are selected, i.e., the TS method. IfM′<M, M′ segments for generating a more compact data set (i.e., asmaller intra-class variation) are employed for prediction. In applyingthe majority voting for a two-class problem, an odd number of predictorsshould be used for decision combination. Consequently, different M′segments are selected for voting and the results are shown in FIG. 26.It is observed that when M′ is 3, SVM performs the best and ELM canachieve good results as well. Furthermore, with the increment of M′, thenumber of samples in the data set increases. Therefore, M′ is set as 3in order to maintain a simple but effective prediction system forclinical usage.

Comparison of Different Predictive Strategies

The predictive strategies are summarized as follows and illustrated inFIG. 27.

-   -   Global: The HRV measures are calculated from the entire RR        interval sequence where the length of signal varies from 2273        beats to 21697 beats.    -   Local: The HRV measures are calculated from a local sequence        which is the last portion (2250 beats long) of the original        signal.    -   Total segment: All non-overlapped segments in the local sequence        are used for prediction by the majority voting rule. In this        study, each segment is 250 beats long, and therefore 9 segments        per patient are obtained from local sequence.    -   Selective segment: M′ selected non-overlapped segments in the        local sequence are used for prediction by the majority voting        rule. Since M′ segments are selected, signal of M′×250 beats        long per patient is used for analysis.

As seen in FIG. 28, in some cases the Global strategy outperforms theLocal strategy, and vice versa in other cases, but the best results areachieved by using the selective segment method.

Experimental Data Set 2

In another study, eight vital signs and raw ECG data were acquired fromcritically ill patients at the Department of Emergency Medicine (DEM),Singapore General Hospital (SGH). These vital signs include temperature,respiration rate, pulse, systolic blood pressure (SBP), diastolic bloodpressure (DBP), oxygen saturation (SpO2), Glasgow coma score (GCS), andpain score. The ECG signals are acquired using LIFEPAK 12defibrillator/monitor and downloaded using the CODESTAT Suite. To ensurethat qualified RR intervals are used for calculating HRV measures, onlycases containing more than 70% sinus rhythm are included in the dataset. In summary, 100 patients are chosen for analysis, among which 40cases are died and 60 cases are survived to discharge.

In the data set, each patient is represented as a 24-dimensional featurevector (16 HRV measures and 8 vital signs) and the corresponding outcomeis coded as either 0 (survived to discharge) or 1 (died). In theexperiments, 75 patients are randomly selected for training and theremaining 25 patients are used for testing. This procedure of partitionand classification is repeated 50 times, and the final results are theaveraged output values. However, random selection of samples may resultin unbalanced training and testing sets, we therefore do the randompartition for each class individually so that 75% samples in class 0 and75% samples in class 1 will go into the training set in each iteration.

Prior to implementing ELM for classification, min-max normalization isperformed to transform the feature set into the interval [−1,1], and thenumber of hidden neurons is heuristically determined as 30. Furthermore,sensitivity, specificity, and classification accuracy are calculated toevaluate the predictive performances. In the following, experimentalresults are reported and analyzed.

Segment Based Analysis of Patient Outcome

Within the data set of 100 patients, the length of RR interval variesfrom 2273 beats to 21697 beats, hence the maximal length of localsequence is 2273 beats. The local sequence was divided into 9 segments(M=9), each of which was 250 beats long. By applying the segment basedpredictive strategy, the classification results using vital signs, HRVmeasures, and combined features are presented in FIG. 29. It can beobserved that the best results (Accuracy: 70.88%, Sensitivity: 47.93%,Specificity: 78.92%) are obtained using combined features with sigmoidactivation function, and prediction of mortality with either HRVmeasures or vital signs is not satisfactory. When vital signs and HRVmeasures are used individually, higher sensitivity is achieved by HRVmeasures, whereas vital signs outperform in prediction specificity. Fromthe FIG. 29, it is observed that combining the HRV measures and vitalsigns can generally improve the performance of prediction.

In practice, the number of hidden nodes in ELM usually controls thenetwork complexity and learning performance, and thus may affect thefinal results.

FIGS. 30, 31 and 32 depict the performances of ELM in terms of differentnumber of hidden nodes. In FIGS. 30 to 32, the following activationfunctions were respectively used: hard limit, sigmoid and sine.

It is seen that good prediction results are obtained when the number ofhidden nodes varies from 20 to 30 regardless of activation functions. Wealso observe that the best results are obtained using 30 hidden neuronswith sigmoid function. Moreover, as seen in FIG. 29, both training andtesting with ELM can be accomplished within several milliseconds.

Comparison of Different Predictive Strategies

The three predictive strategies used according to the way that the HRVmeasures are calculated from the ECG signal are the global, local, andsegment based methods. Detailed descriptions of these strategies are asfollows.

-   -   Global based method: The HRV measures are calculated from the        entire RR interval sequence.    -   Local based method: The HRV measures are calculated from a local        sequence to represent the patient.    -   Segment based method: All non-overlapped segments in the local        sequence are used for prediction with majority voting rule.

It is obvious that one set of features are used to represent the patientwhen the global and local strategies are implemented, while M sets offeatures are calculated for one patient if the segment based method isadopted. As seen in FIG. 33, the local strategy outperforms the globalstrategy, and the best results are achieved by the segment based method.

What is claimed is:
 1. A patient survivability prediction systemcomprising: a first input to receive a first set of parameters relatingto heart rate variability data of a patient; a second input to receive asecond set of parameters relating to vital sign data of the patient; amemory module to store the first set of parameters and the second set ofparameters; at least one processor coupled to the memory module andconfigured to analyze the first set of parameters and the second set ofparameters and output a prediction on the survivability of the patientover the next 72 hours based on the first set of parameters and thesecond set of parameters; and a display coupled to the at least oneprocessor to display the prediction on the survivability of the patientover the next 72 hours.
 2. The patient survivability prediction systemof claim 1, wherein the patient survivability system further includes athird input to receive a third set of parameters including at least oneof demographic information of the patient and medical historyinformation of the patient, wherein the at least one processor isfurther configured to analyze the first set of parameters, the secondset of parameters, and the third set of parameters, and output theprediction on the survivability of the patient over the next 72 hoursbased on the first set of parameters, the second set of parameters, andthe third set of parameters.
 3. The patient survivability predictionsystem of claim 1, wherein the patient survivability system furtherincludes a third input to receive a third set of parameters includingdemographic information of the patient and medical history informationof the patient, wherein the at least one processor is further configuredto analyze the first set of parameters, the second set of parameters,and the third set of parameters, and output the prediction on thesurvivability of the patient over the next 72 hours based on the firstset of parameters, the second set of parameters, and the third set ofparameters.
 4. The patient survivability prediction system of claim 3,wherein the demographic information includes at least one of an age anda gender of the patient.
 5. The patient survivability prediction systemof claim 4, wherein the vital sign data includes at least one of a bloodoxygen saturation level of the patient, a temperature of the patient, arespiration rate of the patient, a pulse rate of the patient, a systolicblood pressure of the patient, a diastolic blood pressure of thepatient, a Glasgow Coma Scale score of the patient, and a pain score ofthe patient.
 6. The patient survivability prediction system of claim 5,wherein the heart rate variability data includes at least one of timedomain data, frequency domain data, and geometric domain data.
 7. Thepatient survivability prediction system of claim 1, wherein the vitalsign data includes at least one of a blood oxygen saturation level ofthe patient, a temperature of the patient, a respiration rate of thepatient, a pulse rate of the patient, a systolic blood pressure of thepatient, a diastolic blood pressure of the patient, a Glasgow Coma Scalescore of the patient, and a pain score of the patient.
 8. The patientsurvivability prediction system of claim 1, wherein the heart ratevariability data includes at least one of time domain data, frequencydomain data, and geometric domain data.
 9. The patient survivabilityprediction system of claim 1, wherein the heart rate variability dataincludes time domain data, frequency domain data, and geometric domaindata.
 10. The patient survivability prediction system of claim 1,wherein the patient survivability prediction system is constructed andarranged to be carried by the patient in a wearable configuration. 11.The patient survivability prediction system of claim 1, wherein theprediction on the survivability of the patient over the next 72 hours iseither death or survival of the patient.
 12. A patient survivabilityprediction system comprising: a signal acquisition module having a firstinput to receive electro-cardiogram (ECG) signals from a patient and asecond input to receive at least one physiological signal from thepatient other than the ECG signals; a signal processing module, coupledto the signal acquisition module, to receive the ECG signals anddetermine heart rate variability data of the patient based on the ECGsignals; a vital sign module, coupled to signal acquisition module, toreceive the at least one physiological signal and determine vital signdata of the patient based on the at least one physiological signal; andan analysis module coupled to the signal processing module and the vitalsign module configured to analyze the heart rate variability data andthe vital signal data and output a prediction on the survivability ofthe patient over the next 72 hours based on the heart rate variabilitydata and the vital sign data.
 13. The patient survivability predictionsystem of claim 12, further comprising a patient information module,coupled to the analysis module, to receive at least one of demographicinformation of the patient and medical history information of thepatient, wherein the analysis module is further configure to analyze theheart rate variability data, the vital signal data and the at least oneof the demographic information and the medical history information andoutput the prediction on the survivability of the patient over the next72 hours based on the heart rate variability data, the vital sign data,and the at least one of the demographic information and the medicalhistory information.
 14. The patient survivability prediction system ofclaim 13, wherein the at least one of the demographic information andthe medical history information includes both the demographicinformation and the medical history information, and wherein thedemographic information includes at least one of an age, a gender, andan ethnicity of the patient, and the medical history informationincludes at least one of a Glascow Coma Scale score, a history of anymedical conditions including diabetes, high blood pressure, andmyocardial infarction, and any specific medications the patient isusing.
 15. The patient survivability prediction system of claim 13,wherein the signal processing module and the analysis module areimplemented in a digital signal processor, and wherein the patientinformation module is implemented in a general purpose processor. 16.The patient survivability prediction system of claim 12, furthercomprising at least one of a temperature sensor, a respiration ratesensor, a pulse rate sensor, a blood pressure sensor, and a blood oxygensaturation sensor to provide the at least one physiological signal tothe signal acquisition module.
 17. The patient survivability predictionsystem of claim 12, wherein the heart rate variability data includes atleast one of time domain data, frequency domain data, and geometricdomain data.
 18. The patient survivability prediction system of claim12, wherein the vital sign data includes at least one of a blood oxygensaturation level of the patient, a temperature of the patient, arespiration rate of the patient, a pulse rate of the patient, a systolicblood pressure of the patient, a diastolic blood pressure of thepatient, a Glasgow Coma Scale score of the patient, and a pain score ofthe patient.
 19. The patient survivability prediction system of claim12, wherein the heart rate variability data includes time domain data,frequency domain data, and geometric domain data.
 20. The patientsurvivability prediction system of claim 12, wherein the patientsurvivability prediction system is constructed and arranged to becarried by the patient in a wearable configuration.